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Questions related to Atmospheric Sciences
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Recently,I know that there are many deep learning in atmospheric science field,I am interested in how to use the deep learning in prediction of drought and how could I start to learn machine learning
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Predicting drought using deep learning involves utilizing neural networks to analyze various environmental and meteorological data, such as precipitation, temperature, soil moisture, and vegetation indices. These models employ time series analysis, convolutional neural networks (CNNs), or recurrent neural networks (RNNs) to capture complex patterns and dependencies in the data. By training on historical drought records and associated variables, deep learning models can forecast drought conditions, providing valuable insights for early warning systems and resource management. Regular model updates and integration with real-time data sources enhance accuracy, aiding in proactive drought mitigation and resource allocation efforts.
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Dear all,
Please how can we run seasonal trajectories (e.g. backward) of several years using the GUI of HYSPLIT model? In which option of GUI where we can combine several months (in order to create seasons: DJF, MAM, JJA and SON)
Hope to hear from you.
Kind regards.
Didier
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I am trying to combine JJA for two consecutive years using HYSPLIT. Can anyone please guide how to do it in an easy way?
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Hello,
The terms on the RHS represent the subgrid-scale, and the ones on the LHS represent the large scale, yet both are coarse-grained-averaged. So why do the terms on the RHS still represent the subgrid scale?
Thanks
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Both sides of an equation are equal when they are equal to a fixed value
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This article confirms that only a small part of the fallout from volcanic plumes is by individual greins falling with terminal velocity through the air below the main plume.
The main fallout is in streaks, that is vertical flow of ash laden air, actually a gravitational ash laden air current created by aggregation of ash in the main plume . Same kind of gravitational flow has been observed in heavy rain- and hail-storms.
The physical explanation of this phenomen is when grains flow through air, the flow resistance they drag the air with them. Further description and a measurement of a large streak fallout in Sakurajima, Japan, can be found in:
Eliasson, Jonas, Junichi Yoshitani, Konradin Weber, Nario Yasuda, Masato Iguchi, and Andreas Vogel. "Airborne measurement in the ash plume from Mount Sakurajima: analysis of gravitational effects on dispersion and fallout." International Journal of Atmospheric Sciences 2014 (2014).
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The aerosol aspect mentioned in my first comment is discussed in the following paper
This paper is in my opinion a first study addressing the coagulation aspects, according to the Authors: “For the first time in a plume model, the full Smoluchowski coagulation equation is solved….”
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I know that some people used a simple index to define the Hadley cell intensity, which is the maximum value in the meridional mass streamfunction within the Hadley cell. However I am wondering whether this definition is too simple, if the meridional span of Hadley cell shrinks due to some reason, could we say that the intensity increases at the same time? Any alternative definitions?
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We all know that the process of teaching and learning is a philosophy. Therefore, educational institutions are interested in finding the best means and tools that make the learner receive lessons in an effective and thoughtful manner, taking into account the factors of speed and accuracy. Meteorology is a physical science concerned with the atmosphere in which humans live, just as fish live in the sea. Weather phenomena are processes that occur in a large laboratory, which is the atmosphere, in which many factors that take place together influence each other. Being a teacher, learner or new meteorologist, what is the most important topic that should be focused on and understood?
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Dear Sir Thaer Roomi
Thank you for your interesting answer. I agree with you that the term fluids is used colloquially to describe a large class of continuous systems, this nomenclature follows the Euler’s presentation to the Berlin Academy of Sciences In 1757. It is interesting to mention that the mathematical formulation of Newtonian mechanics is also due to Euler as indicated by Clifford Truesdell:
"We may justly wonder that it took more than sixty years for so simple an extension of Newton’s ideas, but the literature of mechanics does not permit us to doubt that it did. As often happens in the history of science, the simple ideas are the hardest to achieve; simplicity does not come of itself but must be created".
I fully agree about a fundamental importance of a sound knowledge of the vertical structure. When talking about atmospheric stability I would like to suggest to study the collected works of Max Margules, they stand today as a prime example of an elegant connection of fluids with thermodynamics. He also contributed to many fundamental theories in dynamic meteorology including the jet stream and the Rossby waves; according to Platzman (1968):
“Margules’ investigation of the tidal equations was the first in which the global planetary wave was explicitly studied from the standpoint of applications to meteorology. It was not taken up again from this point of view until the late 1920’s and early 1930’s when the Leipzig school enlisted it in an attempt to find a theoretical basis for numerous empirical periodicities then believed to exist in meteorological data, ranging from a few days to 37 or more days.”
From Platzman, G. W., 1968: The Rossby wave. Quart. J. Roy. Meteor. Soc., 94, 225–248.
Perhaps we should introduce a new name: Margules-Haurwitz-Rossby waves or even simpler MHR waves.
Please consult also the following site:
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Hello all,
I am a research scholar in the field of atmospheric science. I am willing to discuss with you about convection.
Convection is an important process in reality and in the weather-climate models. There are commonly two ways of treating convection: quasi-equilibrium (QE) convection and non-QE (e.g., triggered) convection. In the QE convection category, the convection may be considered to be in a state of statistical equilibrium with the large-scale convection. While for triggered-like non-QE convection, high-frequency (> 0.5 CPD), small-scale waves will interact with the convection, in which QE does not work anymore.
Can anyone kindly explain more for me comprehensively the difference between QE and non-QE convection?
I appreciate any answers from you!
Thanks,
Yuntao Wei
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Correction:
Sorry, it should read: "In the QE convection category, the convection may be considered to be in a state of statistical equilibrium with the large-scale circulation."
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Whenever I search for papers on google scholar regarding my topic i.e. 'Economic Evaluation of the Impact of Air Pollution on Public Health in Delhi', most research papers are based on Atmospheric Sciences. I intend to focus my review on the aspect of Economics. Kindly help me with the most relevant search engines or techniques, that might make my search for relevant articles easy.
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If you have one or more papers related to your topic, I suggest you mine the bibliographies for leads. You will begin to find the researchers writing on the topic and who is in their network. Continue this process until you no longer find new names.
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Atmospheric science
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Dr. Abdelgadir Abuelgasim : I found similar question asked earlier in this forum. Hope you can get your answer: https://www.researchgate.net/post/What_does_the_negative_values_of_Angstrom_Exponent_correspond_to
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remote sensing
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They are absolutely different. DN is the digital numbers recorded in the raw data satellite data. DN values are converted into radiance and further reflectance values. Both the two parts (atmospheric reflectance and surface reflectance) are contained in the top of atmosphere (TOA) reflectance. Atmospheric reflectance is the sum of reflectance of aerosols and Rayleigh contributions.
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I've calculated Direct Aerosol Radiative Forcing(DARF) values (W/m2) for Ahmedabad and Gandhinagar City, Gujarat, India using SBDART Model (AOD values as an input) at Top Of Atmosphere (TOA), Surface (Surf) and net Atmospheric Radiative Forcing(Atm).
Please let me know how to interpret these values and how to further analyse the data.
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Hi... Yash
Aerosol Direct Radiative Forcing (ADRF) depicts the scattering and absorption of solar radiation by aerosols. the positive values of ADRF indicate the warming and negative values indicate the cooling.
I hope that you have calculated ADRF using up and down radiative flux values. SBDART only provides up and down radiative fluxes later you have to calculate radiative forcing using those values. This method is already published by the many aerosol experts. You can refer to those publications.
Estimated TOA and SFC ADRF indicated the change in flux at TOA and SFC by aerosols. ADRF in the ATM indicates the change in flux within the atmosphere.
Further, you can more clearly associate these estimated radiative forcing to the climate by calculating heating rate. heating rate depicts the warming of the atmosphere. This can easily associate with regional or global climate change.
Herewith I have given you a very basic idea, but I will suggest you to read the related publication and their interpretation. This will give you more idea.
Hope this will help. Feel free to ask me, if you have anything
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I am interested in knowing about the lifetime of a chemical species in the atmosphere. What are the techniques that can be applied in order to estimate the lifetime of a certain chemical species in the atmosphere based on the physical and chemical properties at different length and time scales? Is there any analytical or computational technique that can be used to estimate within limits of permissible errors? or can it be analysed from Earth Observation data?
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In the Lagrangian frame of reference, the residence time of a given quantity of chemical species is determined by the location of the emission, the flow path, the lateral mixing, the vertical diffusion, the deposition (wet and dry), chemical reactions and the transfer between the gas phase and particles. The solution of the mass conservation equation for a given trajectory leads directly to the estimation of a residence time.
Considering the fact that all the parameters of the considered system exhibit a significant variability, we should look at the problem of the residence time more in the sense of the distribution of the residence times evaluated for the ensemble of the trajectories. Calculations can be accomplished using a Lagrangian model that includes all the relevant processes indicated in the first sentence
In the Eulerian frame of reference, we can use a method suggested by Professor Bolin and his coworkers the article: "Residence time of atmospheric pollutants as dependent on source characteristics, atmospheric diffusion processes and sink mechanisms"
When considering the modifications of the method suggested in this paper, it is advisable to use a three-dimensional transport model with a realistic Planetary Boundary Layer scheme, good schemes for dry deposition coupled to a cloud model. In my opinion, we are still trying to master three-dimensional transport models with the properties required to make realistic estimates of residence time.
The remaining question is: which method is better?
In my opinion, the most accurate and realistic methods are based on the ensemble of trajectories because of their ability to estimate the distribution of the residence times.
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Dear Colleagues/Researchers,
I'm trying to find out the physics how a fast and slow (separately) moving Hurricane/Tropical Cyclone/Typhoon influence storm surge generation. But so far I haven't found any detail explanation except some generalized statements.
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One of the element to consider is the shape of the seafloor in all the questionable cases. I would perform first a theoretical study with the shallow water system before applying a complete 3D model.
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I am a third year Mechanical Engineering student from Sardar Vallabhbhai National Institute of Technology Surat, India. I am interested in pursuing higher studies in the field of space sciences, in particular study of planetary hydrodynamics, astrophysical plasma and electrical activities in planetary atmospheres.
I am looking for universities as I want to pursue higher studies and a career in the field mentioned above. Please give your suggestions and recommendations in the following format.
Name of university
Location
Research group, associated research (current, past and future prospects)
Admission criterion
Website
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Paul: Master or PhD degree in Space Science (or related field)
1. University College London
2. Politecnico di Milano
3. The Open University
4. KU Leuven
5. University of Leicester
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Cloud morphology and cloud movements are often associated with nature of flow in a planetary atmosphere both locally and globally. They have us an inference about the atmospheric circulation.
Atmospheric transport processes are associated with both molecular dispersion and bulk transport of mass, momentum and energy.
Turbulence is associated with chaotic behavior of fluids in motion. In this regard, I would like to ask what are the effects of turbulent flow on the cloud morphology, both at macroscopic scales (i.e. features observable to naked eyes) and microscopic domain (aerosol and ion transport).
I read some texts on the nature of turbulence, and Kolmogorov scales. Is there any way to possibly estimate the effect, both in qualitative and quantitative manner?
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Please look the attachment, may be it will be interesting for you.
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Hello,
Kindly suggest me regarding the following:
Can we install and run WRF using virtual cpu's with the help of intel compilers. If yes, what is the minimum number of virtual cpu's are required to  generate 72 hr forecast run with 15 mins temporal resolution and 9 km spatial resolution for 400 * 400 grid points for real time forecasts?.
Awaiting your reply,
Thanks & regards,
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As Kishore Ragi mentioned, there are a lot more considerations for compute running time. Also, are you considering running in a container on a local machine or cluster, or do you mean virtual CPUs on a cloud computing platform? Siuta et al (2016) did a viability study on Google Cloud that attempted to benchmark their systems for forecasting tasks. They include pricing information as well:
Siuta, D., G. West, H. Modzelewski, R. Schigas, and R. Stull, 2016: Viability of Cloud Computing for Real-Time Numerical Weather Prediction. Weather and Forecasting, 31, 1985–1996, https://doi.org/10.1175/WAF-D-16-0075.1.
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Unlike Optically thick clouds, Cirrus Clouds are thin, high altitude clouds formed in the upper troposphere layer of the Earth's Atmosphere. These Cirrus Clouds are not easily identifiable in the satellite images acquired with Passive Remote Sensing Sensors such as Landsat MSS, TM, ETM+, ASTER, SPOT, etc. Although there are different kinds of Cirrus Clouds, the sub-visible Cirrus Clouds are particularly of interest because they can be hiding in plain sight and affect the measurements. However, they can be detected within the Short-wave infrared (SWIR) portion of the electromagnetic spectrum, specifically at ~1.38 µm bouncing off of the ice-crystals in these clouds but are absorbed by water vapor in the lower part the atmosphere. Due to the benefits of this wavelength at 1.38 µm, MODIS (1999 onwards), VIIRS (2011 onwards), Landsat 8 (2013 onwards) and Sentinel 2 (2015 onwards) were introduced with their respective Cirrus Cloud detection bands.
However, in the absence of Cirrus detection bands in passive satellite sensors operating before 1999, is there anyway to pin-point the presence of Cirrus Clouds in historical satellite images? It may be possible to identify Cirrus Clouds in satellite images acquired without cirrus band by comparing it with contemporary/concurrent satellite images acquired with sensors having cirrus band. But otherwise, is there any other alternative way? Is anybody aware of any operational tool/algorithm/products that can identify cirrus clouds in past satellite imagery and provide means for their masking/correction?
This topic may be of particular relevance in time-series studies where historical satellite images are frequently compared with the present. For example, if cirrus scattering affects are not corrected, they can lead to incorrect interpretation in Vegetation Indices such as NDVI.
Sources:
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It really depends on the type of imaging. If, as mentioned before, there is spectrally resolved information, one can make use of the fact that the contribution functions of some channels peak at different altitudes and apply some kind of minimization technique (e.g. see our algorithm for IR sounders https://bit.ly/2VAG2NZ).
However, if the image was obtained only at one wavelength or the contribution functions are too broad or they do not "scan" the whole altitude range then it's more difficult. One can train neural network using this kind of images and a combination of active and passive sounders (e.g. AIRS + CALIOP) serving as a reference, but it's easier to say than to make :)
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I am looking for sources to learn about the computational methods applied to study atmospheric sciences. I have been learning computational fluid dynamics i.e. finite element and finite difference methods. Where should one start if one wants to study and get a good hold over the computational approach?
I have come across many statistical methods to study rarified medium and radiation matter interactions. Can anyone suggest a few sources regarding the same?
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This reference
Durran, Dale R. Numerical methods for wave equations in geophysical fluid dynamics. Vol. 32. Springer Science & Business Media, 2013.
could be a good starting point. The point mentioned in the previous post that geometries in atmospheric flows are simple is debatable, as soon as you want to consider orography and avoid coordinate transformation in the vertical direction.
This does not mean that spectral-like methods cannot be applied, but the recent trend is more towards spectral elements or high order finite elements (often DG) rather than the traditional spectral approaches, which however have had historically a key role in improving atmospheric models in the 70's and 80's.
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I am looking for video, exercise, practice options, books and tutorial of python for atmosphere and ocean science (climate, hydrology, hydro climatology, cryosphere).
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Alastair Bain McDonald thank you and I will try these
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When polar jet stream has southward shift mandering happens, it leads to polar vortex.
It is also true that the magnitude of polar vortex depends on the temperature difference between poles and mid-latitudes.
Moreover, the southward shift of jet stream is related to the southward shift of ITCZ (Intertropical Convergence Zone)
Then, Why polar vortex is not a regular phenomenon?
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Totally agreed with Matthew Mabey . It is a regular phenomenon but complicated and a broad field of study. Just let's add a comment about its dynamics. One fundamental feature of polar vortex is its nonlinear evolution of its instability in diabatic and adiabatic environments. The growth of instability and the most unstable mode of a polar vortex in gamma plane can define the crudest features of the vortex. The stationary state of zonal velocity in 'geostrophic balance' can specify the most unstable mode based on the linear stability analysis that is not universal and varies for each planet.
For more information and having a big picture about underlying dynamics of polar vortex and polar jets on Earth and other planets like Mars or Saturn and vortex in general, I propose to read some relevant articles like:
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Hi,
Can anyone please specify the HPC or server configuration details (including price, area and other factors into account) to run WRF-ARW over India (50 E to 110E and 10S to 45N) at least at 9 km resolution daily four times to generate 72hrs forecast. Please specify which compiler will satisfy the needs. If possible with gcc/gfortran, what is the minimum HPC or server configuration required including the price?
Thanks & Regards,
Malleswararao M
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At 9km for the mentioned lat-lon, you will have around 672x720 grids. This requires not less than 200 cpus daily for one simulation.So, to run 4 simulations in parallel you may need more than 1000 cpus. In WRF you can use DMPAR "Distributed memory parallelism" that runs accross nodes. DMPAR uses MPI unlike the SMPAR, "shared memory parallelism" which uses OpenMPI. Concerning the prices, it varies from one vendor to the other and you can check one which you want online.
Good luck,
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Dear RG members
I want to know about a change/changes in atmosphere just above the surface of the Earth can be observed very prior to earthquake initiation.
Regards
IJAZ 
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the professor from Russian State Hydrometeorological Univercity I Bokov studied in n in the field and there is a special a special site for earthquake forecast. try to find his site
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I am getting all other data sets like Relative Humidity, U-wind, V-wind etc. in NCEP site (https://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.pressure.html), But I am requiring Relative Vorticity in pressure levels. Can someone shed light on whether such product is available or not ?
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Here you can get the original NCEP/NCAR Reanalysis data, updated to present, and can also subset in space or time [see Data Selection tab]:
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Dear All This is Suman from IIT Kharagpur, India. I have a very silly query which described as follows. Any sort of insight is will be appreciated. Harmonic analysis is a basic mathematical technique used in various branches. In the field of meteorology and atmospheric sciences, we regularly use harmonic analysis for the elimination of annual (1st harmonic) and semi-annual (2nd harmonic) variation from different data. From the earlier literature it is noticed in most of the cases long term series of monthly data is used for that purpose. For example if there is a complete series of 30 (1951-1980) years monthly data (30*12=360 months), then to eliminate seasonal variability of the original data 1st, 2nd and sometime 3rd harmonics will be subtracted considering a Fourier series of 12 harmonics (for 12 months). Please make me correct it I am wrong. In my case, it is something similar but I don't have whole data as I am interested in seasonal study (Indian Summer Monsoon). I have also 30 years of data having only June, July, August and September. It means the data I have June, July, August of 1951, June, July, August of 1952 June, July, August of 1953 and so on. Now if I want to eliminate the seasonal harmonics then what to do? As I don't have data for all months, it is unscientific to use the above procedure for my data. Is the idea is only applicable for complete data (I mean having all months data)? Please help. Suman Maity Ph.D student IIT Kharagpur India.
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Dear Suman,
I am not sure to understand very well what you want. If you just want to filter out some frequencies, it may be good to use a spectral filter. I would suggest the Lanczos' one: https://journals.ametsoc.org/doi/10.1175/1520-0450%281979%29018%3C1016%3ALFIOAT%3E2.0.CO%3B2
If you definitely need to use your method, I would say that for this you cannot just use up to three harmonics on your data. Actually, since it is a 4-month period, the first harmonic gives a single cycle over the 4 months ( a 4-month periodicity) , the second harmonic 2 cycles (a 2-month periodicity), the third harmonic 3 cycles (1.3 month periodicity)... Thus considering only the first harmonic should eliminate your "seasonal" cycle.
For more documentation, please go through "Time Series Analysis in Meteorology and Climatology: An Introduction" by Duchon and Hale.
As for software, NCL is the easiest to use in my sense. However, other languages including R and Python can do the job.
Kindest regards
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If these organisms' threshold is exceeded, in the long run, will it develop evolutionary defenses or mechanisms to absorb too little or too much CO2?
This article says that the earth's CO2 level rose to 400ppm. I want to know its effects on upwelling, both in the short and long run. Also its effect on photosynthetic organisms which love CO2, and more importantly chemosynthetic organisms, which will have an abundance of CO2.
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Through rise of temperature
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Machine Learning as a tool for Meteorology.
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Hello Mukesh, in Ireland we used machine learning for removal of systematic errors (i.e., systematic bias removal) from windspeed forecasts for wind-farms. As forecasts are made and then validated, a history of forecast errors builds up over time, and the "machine" can "learn" from this history to identify and then correct what it finds to be systematic errors - leading to systematically more accurate forecasts. We used the R package implementation of Bayes Model Averaging (BMA) to do this. See our papers on this at and You may be able to think of other applications for this (or similar) methods.
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Hello
I'm a student of atmospheric sciences, and I'm analyzing two different hurricanes, I'm using latent heat flux data from NARR, but I'm not sure about the results that I obtain because all of them are negative values, which I interpret as condensation...I've read this from nasa's page https://svs.gsfc.nasa.gov/3199 and they obtained opposite values during the development of hurricane Frances, I don't know if I should multiply by -1 or there's an agreement for the interpretation of the results of this kind of data from NARR?
If I could have your help I'd be very greatful, this is part of the work I'm currently doing
thank you!
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Thanks to everyone for the responses, I saw them and then I started work on your recomendations.
According to Mesinger and Ebisuzaki, the latent heat flux from NARR data has the wrong sign. So if we have negative values they should be interpreted as positive, we have an evaporation process, and the same occurs for positive values(they should be negative).
They didn't change the sign because the users from the eta model are old as well as new.
Hope this can help others that use this kind of data from NARR.
Thanks, again, for your help.
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Hi,
Can anyone provide me the link which are free to download relative humidity (with hourly temporal resolution) with 1 day real time lag?.
Thanks & Regards,
Malleswararao M
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I am only aware of 6 hourly products. Check if it's useful
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Does anyone know how frequently sand storms and dust storms that arise from middle east or north africa travel to Pakistan and North India? I was wondering, in view of the already worsening air pollution levels in North India, events such as dust and sand storms reaching the subcontinent may exacerbate the situation. How rare or common are such sand and dust storms being carried from their place of origin (usually middle east and north africa) and intermix with fog or haze intensified by smoke or other atmospheric pollutants in another far off location? Has there been any similar, possible mixing of phenomena (dust storm and smog) reported/documented/studied anywhere around the globe at any time, preferably that was also caught by polar or geostationary satellites?
I was looking at a true-color or natural color satellite image acquired on 29th Oct. 2017 by the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the joint NASA/NOAA Suomi-National Polar orbiting Partnership (S-NPP) satellite around early afternoon. I've attached a screenshot of the image as well as provided the full link to access the satellite imagery. These satellite images have been stitched together to create a global mosaic. Unlike MODIS, VIIRS do not show any data gaps (except sun glints!). I found this satellite image particularly compelling because it clearly shows the sand storm picking up over northern Saudi Arabia and moving around Iraq, Iran, Caspian Sea towards Afghanistan with the movement of wind. I also think the Earth's rotation from west to east has a role to play in the movement and direction of the wind laden with sand and dust. But it seems difficult to understand their dynamics. The smog over North India and parts of Pakistan can be differentiated from the sand storm over middle east in this satellite image. In North India this is the time of the year when there are intentional crop fires due to the traditional slash-and-burn agriculture practice.
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I need to downscale precipitation and temperature from IPCC4 GCMs (in LARS-WG) for the baseline and future in the Generator menu. Of course it generates future data once u select the GCM and time period but I can't understand the science in that and how to generate for the baseline data so as to calibrate and validate with the observed data. 
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First I must say you can calibrate and validate at analysis tab especially in Qtest. So, seeing the statistical results of the Qtest analysis, you can make sure the LARS-WG is useful for simulation future. Then go to the generate tab and prediction for future period.
Also, the following article may be helpful:
"Simulation of extreme weather events by a stochastic weather generator" by Mikhail A. Semenov Climate Research 2008.
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Dear RG Community, I have the need to estimate the PBL height for the area of Milan (Italy) in two years (2011, 2014) as 12-hours values more or less. These data will be necessary to run a model for the estimation of airborne pollutants, removed by vegetation.
Searching in the web I found this database
but I don't know how and if it is "easily" possible to calculate PBL from them. I will be very gratefull if some collegue can help me to better deepen the problem. Thanks in advance.
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Hi Valerio,
I am sure you must have figured out a solution by now (since you posted the question 2 years back). However, here is what I found useful for calculating PBLH using the sounding data from the above mentioned website. Observed Diurnal Cycle Climatology of Planetary Boundary Layer Height, Liu, Shuyan; Liang, Xin-Zhong, Journal of Climate, vol. 23, issue 21, pp. 5790-5809. The authors have used a FORTRAN code that they developed for calculating PBLH using the sounding data.
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Hi
I am comparing two rainfall datasets at different time scales (e.g. daily, monthly and annual). I am using mean error, mean absolute error and root mean square error. One way to make comparison is using mm/day for daily, mean mm/day for monthly and annual scale. But I am using accumulated rainfall, as a result, the error statistics is not comparable at different scales (error increasing as moving from daily towards annual scale). Kindly suggest how to normalize my error statistics to make it comparable.
Thanks
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Thank you Saseendran.
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Dear community,
I have measurements of pressure (p), temperature (T) and relative humidity (i.e. water vapour content, RH) and I'd like to know if there's a simple way to calculate atmospheric transmissivity from these quantities, and for the transverse (i.e. horizontal, same altitude) direction. I've looked at several atmospheric radiation models such as Modtran but these are all for depth-integrated calculations, and it seems to me that there must be a simple way to do this if we don't need to take into account the varying optical properties of the different atmospheric layers. Are these three measurements (and assumptions for the concentrations of other atmospheric gases such as 400 ppm CO2) sufficient? I'm specifically interested in transmission in the thermal infrared band (7.5-13.5 microns). Thanks for any help that you can provide.
Regards,
David Jessop
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Getting behind the scenes means having access to absorption coefficients for the molecules with bands in that region, plus water "continuum" absorption (generally accumulated line tails from more distant bands I believe). You also haven't mentioned aerosol extinction - perhaps you have access to local visibility data, though most airports don't report any meteorological range greater than 20 km... Aerosols aren't a single radius so a simple Mie calc isn't sufficient. I haven't looked for simple models since I've used Modtran and predecessors for 40 years...
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Reading the related literature, although there were lots of paper regarding the effect of humidity on the output performance of PV panels, I have just seen two paper mentioned the effect of humidity on the accumulation of dust.
DOES ANY ONE KNOW ANY FURTHER STUDY OR CAN SHARE ANY INFORMATION IN THIS REGARDS?
1. According to the research done by Oguntoke et al [1], atmospheric humidity had negative correlations with dust fall. Mean relative humidity below 50% and mean wind speed above 4 m/s were predicted as critical levels for dust episodes incidence at sites that recorded “heavy” and “very heavy” dust fall.
2. Said and Walwil [2], showed that the adhesion increases with increase in humidity due to the presence of condensed water in the gap between the particle and surface which forms water capillary bridge between the bead and the surface. So dew drops in the morning as well as the amount of humidity could influence the process of dust accumulation.
Reference:
[1] Oguntoke, O., Ojelede, M. E., & Annegarn, H. J. (2013). Frequency of mine dust episodes and the influence of meteorological parameters on the Witwatersrand area, South Africa. International Journal of Atmospheric Sciences, 2013.
[2] Said, S. A., & Walwil, H. M. (2014). Fundamental studies on dust fouling effects on PV module performance. Solar Energy, 107, 328-337.
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The adhesive behaviour is likely to be complex and variable, depending on what the dust is made of, how long it remains in contact and a bunch of other factors. The negative correlation is likely to be due to the fact that humid places have higher rainfall, so the modules are washed clean more frequently. SERIS is doing long term in-situ measurements in several locations around the world to get real data on these phenomena.
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I was trying to plot the global (spatial) distribution of a certain parameter (let's say temperature or precipitation). What I found that plotting a time averaged figure would not be effective in my case. I want to understand how a certain parameter is changing globally/ regionally on a temporal scale. Can any one suggest me any idea or plot type for such analysis?
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Having done some GIS analysis, and if I understand your question and goals about mapping in this case, you will always face an issue with averaging.  For example, say you have a temperature measure. That measure covers some amount of space.  Depending on where you make measurements, there is always going to be spatial variation in the temperature measure which could have a lot of parameters depending on the size of the space.  For example, I take a temperature measure at Point X.  How far away (X + n) does that measure reach?  Are there other climate factors within the space parameter (X + n) that could affect the temperature?  In other words, the temperature measure for a space is already being defined as an average for the space, even if you have multiple measures within that space.  If there is one measure, you are assuming it has some space scope, and it is the average for that space.  So, this can't be avoided at some level of conceputalization or analysis.
So even if you are using small space (ie. as in a hotspot analysis), your measure is still representing an average for that space, UNLESS you can argue that the temperature is the true measure of the actual temperature in that given space.
Now, across a given space (X) over a period of time, you could assess changes in temperature. I'm not really sure of the method here as applied to climate change, but NASA has a visual example, using radar images ....https://earthobservatory.nasa.gov/GlobalMaps/view.php?d1=TRMM_3B43M&d2=MOD13A2_M_NDVI
How you would model that statistically -- I'm not that smart!
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Dear,
Please, let me know how can I convert total precipitation unit (at ECMWF data) to mm per month?? ( I downloaded at 00:00 and 12:00 hours)
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First of al you need to extract daily total precipitation. On the ECMWF data server you can select as start times 00:00 (midnight) and 12:00 (midday), both with step 12. This gives you two records:
* accumulated precipitation from 00:00 to 12:00 (midnight + 12 hours)
* accumulated precipitation from 12:00 to 24:00 (midday + 12 hours)
Then sum the two values to get the daily total.
Further, the unit is m. To get mm you need to multiply by 1000. The cdo command would be 'cdo -r -b 32 mulc,1000 ifile ofile'. Finally you can sum the daily values to get monthly sums.
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By preparing carbonate-rich water from deionized water and carbonate salts, the alkalintiy keeps changing over time. I assume there is a non-equilibrium with the atmospheric CO2, thus the constant change over days.
How can I produce synthetic water of known and fixed HCO3- content (at pH 8), avoiding this change of alkalinity over time?
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Hi Bruno,
1. Which question are you ultimately addressing?  What do you want to use the water for?  Chemical or biological experiments?
2. How did the pH change? Was it fluctuating or directional and if the latter, in which way?
3. My experience is mostly re. seawater TA (total alk) and from about 20 years ago: (in line with Christoph's answer)  the classic paper for TA is still the one by Andrew Dickson 1981 for the definition of total alkalinity in seawater (http://scrippsscholars.ucsd.edu/adickson/content/exact-definition-total-alkalinity-and-procedure-estimation-alkalinity-and-total-inorganic-ca-    DOI: 10.1016/0198-0149(81)90121-7 according to which the entry and exit of CO2 does not alter TA but it affects pH.  I remember however, that geologists had their own definition of alkalintiy, for they asked different questions.
4. I would have thought that the 'easiest' way, in principle, is to keep the solution in a closed system at all times, so as not to allow any exchange of CO2 across the air/liquid interface.  If of use: In alkalinity titrations this system was used by e.g. Bradshaw and Brewer (1988) DOI: 10.1016/0304-4203(88)90023-0 
5.  Following on from what Francois has written, the more additional acid-base systems you have in there which do not involve gaseous components, the better (hence Francois mentioned buffers.  However, if you want to keep it as natural as possible, e.g. for biologigal experiments, this is not always favoured).
6. I just remember: when I titrated blanks (just H2O and NaCl, and thus not pH-buffered at all) my precision was the worst when compared to NaCO3 standards and natural seawater - presumably because of gas exchange - for I had a SEMI-closed system only.
7.  I was advised to use Na2CO3 (and dry it and keeping it in a desicator before weighing it) instead of NaHCO3 to increase the accuracy when weighing it.
Kerstin
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When we found the non-zero helicity generation during TC formation in the tropical atmosphere (2010), our next step was about whether this might be favorable for the initiation of  large-scale helical vortex-instability. Indeed, we found  the instability by analyzing the kinetic energy of the primary (tangential) and secondary (transverse) circulation in our works  (2011-2016). Though the conditions in your experiment are quite different from the atmospheric ones, I would try to analyze the kinetic energy too.
References:
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Concerning experimental measurements, we have made a series of experiments with a focus on non-stationary stage of the cyclonic vortex formation. Time dependence of the kinetic energy of tangential flow is very similar to the ones from numerical simulations except the initial stage. In experiment, after the heating is on the meridional circulation forms relatively fast in comparison with numerical simulations. This fact and similarity of the vortex structure (to the atmospheric ones) are strong arguments toward universal scenario of vortex formation, when we have initial weak large vortex (in our case - solid body rotation) and secondary meridional circulation. 
  Now we are analyzing the results and I hope in a month or two the paper would be ready. The connection between helicity and vortex formation is still an open question. We started new numerical simulations using Open Foam in similar to the laboratory experiment statement. Using data from numerical calculations we will try to understand is helicty indeed important for the vortex formation or it is one of topological characteristicsof the vortical flow. Of course we should remember that our flows are rather laminar than turbulent, so we can not study specific turbulent effects when helicity may be of great importance.
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I am examining long-term rainfall records.  We have identified a 22-year cycle (this cycle is well known in South Africa) at our site, and have fitted a wave function to the seasonal (July to June, cumulative) data that spans 128 years.  This gives a wave with 6 peaks (6 full cycles).  
Research question: does the wave function predict the 13 (ca 10%) driest years?
Approach: I calculated that 20.5% of all possible years should fall within the lowest 10% of predicted rainfall values. (This is because of the shape of a sine curve; a linear ‘saw tooth’ function would include 10% of years in the lowest 10% of predicted values.)
Accordingly, it is predicted that, if randomly distributed, there should be 13 x 20.5% = 2.67 seasons within lowest 10% of predicted rainfall values (bottoms of the 'troughs'), and the rest should fall outside these troughs.
Results: It emerges that 7 of the driest seasons fell within these troughs. (I interpret this an an indication that this gives us some prediction capability as to when very dry seasons will occur.)
My question here: How can I determine statistically what would be a higher-than-expected number of dry seasons falling within the troughs?  E.g. would it be four , or five, or what?
I've attached a graph showing the wave function and when the 13 driest years occurred, omitting the original data for simplicity.
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Dear Justin,
I have two points. a clarification and a suggestion.  First is the way you determine the dry years ?  Rethinking about the assumption behind your decision would shed some light.  
I would have gone for a field (stakeholder opinion) analysis on how to define dryness and its thresholds as a verification of the said  assumptions. Simply because the threshold value is subjective.
The other is that if you move out of your sine curve (hope you have established periodicity) by which  you have established a sine curve, and then take a look at the duration curve (probability of exceedence) for the number of years exceeding the threshold..
Hope this would be of assistance.
Rgds
Sohan. 
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The Southern Ocean [40~65S] accounts for about 15% of Earth's surface area. Satellite observations show that the Southern Ocean features near 90% of cloud coverage all year around. My question is what factors make the Southern Ocean so cloudy?
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The Southern Ocean is no cloudier than the North Atlantic or Pacific oceans, see e.g., SWCRE: http://eos.atmos.washington.edu/cgi-bin/erbe/disp.pl?scf.ann.
So the question may be better posed as asking why the midlatitude storm track regions are so cloudy.
I agree that aerosol has little to do with this distribution of cloudiness. Aerosol may have relatively small influences on the brightness of the clouds (e.g. a few percent increases in summer due to more CCN than in winter) but they don't cause clouds to form where none would otherwise have formed. Over the Southern Ocean, aerosol are probably strongly affected by the clouds themselves (drizzle removing droplets and therefore CCN).
The well-used low cloud predictors (lower tropospheric stability LTS, or estimated inversion strength EIS) do seem to have some skill over these regions, see Fig. 6 in Wood and Bretherton (2006, J Climate) for example. EIS is better than LTS in the midlatitudes because it is a better predictor of the strength of the inversion than is LTS (for reasons explained in the aforementioned paper).
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Hello,
I'm looking for a long-term time series of the East Atlantic / West Russia (EA/WR) atmospheric circulation pattern. Ideally the time series would cover the last 200 years with decadal resolution at worst. Do you know any published sediment core, speleothem or whatever proxy from e.g. central Europe or western Russia that would capture this climate pattern and provide information about the long-term EA/WR dynamics?
Thanks for your help!
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OK, looking forward to an analysis of the long-term evolution of the index and what it means to regional climatic changes over time!
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I'm looking for monthly precipitation data for Canada from 2008 to present, preferably in raster format.
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Dear Itiya Aneece,
 You can visit the  European Space Agency [http://cci.esa.int/data] for receiving data
 The data will downloaded for every 15 day .
Regards
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Working on High Resolution Radiossonde Profiles and open for collaborators.
We have a dataset comprising almost 76 hours with launching every 3 hours during 18 UTC 29 May to 21 UTC 01 June 2016.
Follow some plot of raw profile dataset as example.
If you have some interest on co-authoring this job, please be welcome.
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I am interested!
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Recently used method for the measurement of CO2 loading
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Dear Shailesh Kumar,
As is known, the CO2 may be in a liquid state at temperatures from -56.6 ° C to -78.5 ° C at a pressure of about 1000 hPa (mean surface pressure). Such conditions on the surface of the atmosphere almost never exist, except for very rare in some parts of northern Siberia (Russia) and Antarctica (South Pole). At the top of the troposphere and at the beginning of the stratosphere there are such low temperatures, but there is the pressure of a very small. Therefore, for liquid CO2 should be even lower temperature. Also, CO2 has no liquid state at pressures below 5200 hPa. So great pressure does not exist anywhere in the Earth's atmosphere.
The answer to your question is that CO2 in atmospheric conditions rarely almost never exist in liquid phase.
Regards,
Milivoj B. Gavrilov
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Assume at some location clear sky OLR at TOA is 270 w/sq m assuming earth surface emissivity is one. Then say emissivity is 0.92. Then there will be scattering and reflection and some of the reflection will escape. Therefore what escapes is > 0.92 x 270 w/sqm. Say fraction of 270 w/sq m observed is Fr = 0.97 (It should be between 0.92 and  one). 
(a) Is there a name for Fr?
(b) Is there a "best"  value for Fr averaged over earth's surface?
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I can answer my own question I.M.O.
Consider the publications by Feldman, et al http://www.pnas.org/content/111/46/16297.full.pdf
and by D.L. Seidel
From these references if the average precipitable water vapor of the earth is present in the atmosphere, the specularly reflected thermal IR cannot reach the top of the atmosphere (TOA). Therefore,one can assume that if the emissivity on average is, say, 0.95 then the fraction of the incoming IR from the earth's atmosphere that is emitted from the earth's surface is also 0.95 and one does not have to worry about the fact that some of that incoming IR is reflected, as far as observations at the TOA are concerned.
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Its main use will be to gather weather parameters for wildlife research. I would need to leave it for at least 3 months in desertic and temperate environments. I can have access to it quite often so data retrieval or batteries should not be a problem.
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I used 4 Lascar dataloggers ,(https://www.lascarelectronics.com/easylog-data-logger-el-usb-2/)  in the humid montane forest of the Andes. However, two of them were dropped in a ravine and got ruined because they were covered with water. They measured temperature and humidity every hour for 6 months. However, they can't measure precipitation. I do not know of any datalogger that can measure that.
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I have 3-hourly precipitation of TRMM 3B42 for 17 years with HDF format . and I must compare this data with ground gauge station data . is there anybody know which software can I use for this goal?
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Hello,
I would suggest you to use GrADs for this purpose with less time you can get much result,while you can also work with matlab .....As I have done some thing while I was having my summer internship.
Good Luck,
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I want to downscale 13km precipitation data to 500 m precipitation data. I have hourly precipitation data. I just want to downscale spatially. All the predictor I have is at 13 km resolution. Is it possible to downscale these 13 km precipitation data spatially?
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Hi Tasnuva:
You don't specify the format of the file you want to interpolate. If this file is in the netCDF format, you can use the R function 'resample' of package 'raster' to interpolate. First transform your netCDF file to multi-band raster using function 'brick'.
Another way of doing spatial interpolation of netCDF files is by using function 'ncap2' of package NCO. Another package you can use is CDO, see functions 'remapbil' and remapbic' for biiinear and bicubic interpolation respectively.
Hope it helps,
Augusto 
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The KP, CH equations were all obtained from internal wave models. some results are also obtained about the rotating fluid. I want to know whether they are also right for large scale atmospheric motions. Is it possible for us to derive the equations for the original equations by weakly nonlinear method.
And the ZK equation are used to simulate the motion of plasmas when considering the magnetic field, can we obtain a likely result for atmospheric motion for the original primitive equations.
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Ok!
Best regards,
Jean-Claude saut
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I am getting low BC and PM concentration on the days coming after dense fog events whats may be the reason ?
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Dense fog events sometimes result in the increase in PM concentration during the events. Hence the PM level on the days coming after the fog event days could be seen relatively lower than the event days. I think you need to check whether the PM concentrations in the fog event days are much higher than usual or not, so you can make sure the PM concentrations in next day are really low. Please find the attached paper showing the changes in meteorology and PM concentration due to a severe fog event. You will be able to find a couple of similar papers and they would be helpful for you to figure out what you want to know. Good luck.
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I am trying to dimension an activated charcoal adsorber to remove ambient VOCs before the air enters a system where I will collect plant volatiles. Can anyone provide information on the required contact time of air and activated charcoal? How much activated charcoal do I need per volume of air?
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See the book by Cooper and Alley, Air Pollution Control. A design Approach 2nd Ed (1994), Chapter 12, You need to know the solute and the isotherm on coal. But you will find a generailsed isotherm on activated coal in the book. If you need to calculate time between regenaration cycles, see McCabe et al, Unit Operations in Chem Eng, the Chapter on Adsorption. McGraw Hill 1993.You will find there a couple of good examples.
Regards
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I want to do my master thesis on geopotential height investigation over different pressure levels. I have 30 years historic data set, Please guide me what can i do for my master thesis with this geopotential height. My study area is south asia
Thanks Always!
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One are of interest is how a systematic change in geopotential height changes (either positive or negative) the overall effects of wind shear on cyclone formation and destruction.  For the Atlantic Basin Hurricanes there is some evidence that geopotential height is increasing and this is changing the nature of the shearing winds
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In literature I found only slightly negative values for the Angstrom exponent for aerosol optical depth which occurs in presence of coarse mode aerosols. There is one work (Jaroslawski et al. 2003) where they had AODs reaching -10.
How is this for cloud drops?
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Going back to @Sophie original question, first you should note that the definition of Angstron Exponent is:
AOD(L) = AOD(L0) * (L/L0)^(-angstron)
where L and L0 are the two wavelengths. Angstron is thus the negative of the slope of ln(AOD) versus ln(wavelength) and it should be a positive value. The smaller the particle, the larger the angstron. The theoretical limit is +4 which is the wavelength dependence of Rayleigh (or molecular) scattering.
A negative angstron would mean that AOD increases with increasing wavelength, which I don't know how it could happen. Maybe for some weird large particle of a peculiar shape and index of refraction? 
That doesn't mean that you can't measure a negative angstron. Given your experimental uncertainties you can measure a negative value that, within your error bars, is still compatible with a positive exponent. On the other hand, you can also measure a positive angstron value larger than 4.0, given that your errobars make it still compatible with a physical meaningful value  < 4. That is probably what happened with the paper you cited who used 310 and 320nm. These WL are too close and the error in the derivative will be too large. 
What can definitely be negative is the curvature of the ln(AOD) versus ln(wavelength), i.e., the derivative of the angstron. That is, if you fit your AOD data like this:
ln(AOD(L)) = a0 + a1*ln(L)+a2*(ln(L))^2
in this notation, a1 is -1*angstron, and a2 is the curvature.
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Hi,
We know that the standard density of air is 1.225 kg/m3 (at sea level, 15 oC, & 1 atm). The density, however, is not constant as it changes daily according to the local atmospheric pressure, temperature, elevation, and humidity maybe.
For a certain day(i), is it safe to apply the ideal gas law, i.e., ρi=PiM/RT? Is the elevation and humidity taken into account in this formula? Is there a better widely applicable recipes? 
Your shared thoughts and discussions are highly appreciated,
Abdo,
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Dear Abduslan,
Here some fortran code lines for the humid air density. Enter temperature t (°C), air pressure p (hPa) and specific humidity q (Kg/Kg) and you get your density in kg/m3
zt = dble(t)
zp = dble(p)
zh = dble(q)
zra = 287.04D0    !  Bolton, [MWR, 1980] en J/Kg/°K
zrvap = 461.51D0 ! constant for water vapor (J/kg/°K)
zrtt1 = 273.16D0 ! water triple point
zq0 = zrvap/zra-1.D0
zd = zp*100.D0/(zra*(zt+zrtt1)*(1.0D0+zq0*zh))
dens = zd
Regards, G.C.
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 can anyone tell me how equatorial wave, Rossby wave and kelvin wave generate and propagate in atmosphere and what are the time period of these waves?
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Ravi,
waves occur in the oceans and in the atmosphere. Both water and air are fluids so the types of waves in both are the same. The Wikipedia articles describe both, e.g.
"Rossby wave
From Wikipedia, the free encyclopedia
 
Rossby wave, also known as planetary waves, are a natural phenomenon in the atmosphere and oceans of planets that largely owe their properties to rotation. Rossby waves are a subset of inertial waves.
Atmospheric Rossby waves on Earth are giant meanders in high-altitude winds that have a major influence on weather. These Rossby waves are associated with pressure systems and the jet stream.[1] Oceanic Rossby waves move along the thermocline: the boundary between the warm upper layer and the cold deeper part of the ocean."
HTH,
Cheers, Alastair.
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I have been bit confused about the accuracy and precision details mentioned about an Eddy covariance system, which comprises of a sonic anemometer and a C02/H20 measurement.
Usually for a Gill sonic and IRGA-Licor 7500, the accuracy and precision are mentioned like these:
3-D Sonic Anemometer, Gill Solent Windmaster Pro
Orthogonal wind velocities u, v, and w
Range: ± 20 m/s
Accuracy: u,v =1.5% root mean square (RMS) error, w =3% RMS error
Resolution: 0.01 m/s
Sonic temperature (from speed of sound (SOS))
Range: -40 to +60 deg C (307-367 m s-1)
Accuracy: 3% RMS error in SOS
Resolution: 0.02 deg C
CO2 density
Range: 0 to 110 mmol/m3
Accuracy: ~ 1% (limited by calibration procedure)
Precision: ~ 4 umol/m3
(typical RMS instrument noise)
H20 density
Range: 0 to 2000 mmol m-3
Accuracy: ~ 1% (limited by calibration)
Precision: 0.14 mmol/m3
(typical RMS instrument noise)
By reading this, I can understand the RMS noise for the CO2 and H20 measurements are  4 umol/m3 and 0.14 mmol/m3 respectively.
But what about the RMS noise for velocity and sonic temperature measurements? Is it the resolution of u,v and w and T measurements which are mentioned?
I would be very grateful if any expert on EC system can clear this query of mine. 
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The overall errors in flux measurements by Eddy Covariance is about 15% for CO2 flux, about 5-10% for H2O flux and about 5% for sensible heat flux when the setup is very clean and the processing is full and complete.
But is will still vary with instrument height, position of instruments vs each other and wind direction (sonic anemometer shaded or blocked by analyzer), and structure of turbulence for each 30 minutes.
So, overall error is comprised of instrument performance, setup, processing and ambient conditions.
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What are the advantages and disadvantages of Disdrometer for determining rain drop size distribution? And how about other methods like high-speed imaging or etc.? Which methods do you prefer if Disdrometer is not available?
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Dear Zahra,
The following text was taken from a recent review article entitled "Rain Drop Measurement Techniques: A Review " published in Water (MDPI) which illustrates all available methods, their scope and limitations.
Automated Rain Drop Measurement Techniques
Impact Disdrometers
The kinetic energy of rain drops is critical to soil erosion and stormwater pollutant wash off studies because it is indicative of the potential of drops to displace particles normally bound to a surface, causing to soil particles to enter surface water flows. The combination of drop size distribution and drop velocity can provide an estimation of kinetic energy, however there have been several previous attempts to take measurements directly. This has been done using either acoustic or displacement methods.
 Acoustic Disdrometers
Acoustic disdrometers involve the generation and recording of an electric signal via a piezoelectric sensor when drops fall on a specialized diaphragm. Based on the relationship between kinetic energy and drop size calculations, this electrical signal is converted to kinetic energy via the measured acoustic energy. Modifications to the sensors used in acoustic disdrometers by Nystuen et al. enabled use
in marine environments, however difficulties remained during high rainfall intensity measurement. Jayawardena and Rezaur also successfully modified the acoustic disdrometers, and improved drop size distribution, rain intensity and kinetic energy measurement accuracy. Other commercial devices have been successfully developed by Salmi and Ikonen [97], Salmi and Elomaa, Winder and Paulson, Bagree and Vaisala.
Limitations to accuracy in drop size estimation arise using acoustic disdrometers due to the difficulty in obtaining a uniform acoustic response over the entire diaphragm. Difficulties in the accurate measurement of smaller drop sizes also remain because of insensitive diaphragms, and splash effects. In addition, higher intensity storms are not able to be measured due to background noise which decreases measurement accuracy.
Displacement Disdrometers
Energy generated by drops falling on the top surface of a displacement disdrometer is translated via magnetic induction, and converted via electrical pulse to estimate the size of a rain drop. In addition to magnetic induction, several mechanisms have previously been trialled to accurately measure drop size including elastic springs, bonded strain gauges, and pressure transducers. Arguably the most widely used displacement disdrometer is the Joss-Waldvogel Disdrometer which has been commercially available for past 45 years. This unit has undergone several iterations to improve the composition of the cone which is the principle measurement component. Successful modifications have included the addition of a digital converter. Although this disdrometer may have provided advantages such as measurement over a wide range of drop sizes, and the ability to continuously sample over longer durations, limitations remain including accurate drop counting, and accurate measurement of velocity, kinetic energy, intensity, and drop shape.
Optical Disdrometers
Optical technologies (optical imaging or optical scattering) are non-intrusive rain drop measurement techniques. These methods do not influence drop behaviour during measurement, and have successfully resolved drop break up, and drop splatter problems experienced by other measurement methods.
 Optical Imaging
Recent imaging techniques developed have involved two motion cameras (2DVD) to show raindrop microstructure, including front and side drop contours, fall velocity, drop cant and horizontal velocity. General rainfall parameters such as rain intensity and drop size distributions have also been accurately measured. Two motion cameras record images of drops which have been used to accurately measure drop velocity, diameter, and shape (including oblateness. Measurement errors arising from drop drift caused by the tall unit design have led to design modifications, including the development of an indoor model, and one specifically designed for outdoor use.  Liu et al. developed a video system capable of accurate drop shape and velocity measurement. The set up consists of optical and processing units, and a unique imaging unit comprised of a planar array charge-coupled device sensor (CCD).
For the other methods, please see attached file.
Hoping this will be helpful,
Rafik
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It is an observed fact that presence of moisture in atmospheric air brings down the atmospheric pressure. What is the mechanism for this process and scientific reasons associated with this fact?
Scientists from weather,atmospheric science, environment and meteorology can give a clear picture of this phenomenon. 
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Dear Annu, thank you for your answer, you can think in that manner there is no wrong with your assumptions, but more correct is condensing nature of moisture that create vacuum due to more volume of moisture becomes/occupies less volume on condensing with decrease in partial pressure of moisture portion in the atmospheric air is responsible for low atmospheric pressure.
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I am working on a project known as atmospheric water generator,where i am using silica gel as a desicant to extract water vapour from humid air and gets adsorbed.So i want to use that adsorbed water molecules to pure drinking water.Please suggest me the detailed methods on how to do so.
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Silica gel is a granular, vitreous, porous form of silicon dioxide made synthetically from sodium silicate. Silica gel contains a nano-porous silica micro-structure, suspended inside a liquid. Most applications of silica gel require it to be dried, in which case it is called silica xerogel. For practical purposes, silica gel is often interchangeable with silica xerogel. Silica xerogel is tough and hard; it is more solid than common household gels like gelatin or agar. It is a naturally occurring mineral that is purified and processed into either granular or beaded form. As a desiccant, it has an average pore size of 2.4 nanometers and has a strong affinity for water molecules.
Silica gel is most commonly encountered in everyday life as beads in a small (typically 2 x 3 cm) paper packet. In this form, it is used as a desiccant to control local humidity to avoid spoilage or degradation of some goods. Because silica gel can have added chemical indicators and absorbs moisture very well, silica gel packets usually bear warnings for the user not to eat the contents.
Regeneration:
Once saturated with water, the gel can be regenerated by heating it to 120 °C for 1 to 2 hours. Some types of silica gel will "pop" when exposed to enough water. This is caused by breakage of the silica spheres when contacting the water. Evidently this type of silia-gel is to be avoided in use.
When the water is released by heating the silica gel, the only thing to still do is a question of recpaturing water vapour from the regnerating silicac gel on a cold surface, Hence, the heated air has to evacuated from the regenerating silica-gel and moved over a cold spot so that it condenses (hence why not use a good Peltier based condenser right?). Then one can simply harvest the condensed water dripping out of the condenser and use the regenerated siilica-gel to capture water again. It is a kind of  'perpertuum mobile', but evidently driven by the energy (heat) needed to regenerate the silica-gel.
It's that simple.
Good luck, and schol,
Frank
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Hello everyone, I'm working on automation model CMAQ and need to know about tools to make operations with the different chemical species to produce the PM10, PM25 and others, I am interested in to know programs that allows me to automate this work like GrADS for WRF model outputs for example, any ideas?
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You may try open source MeteoInfo/MeteoInfoLab software (http://www.meteothinker.com/) to plot CMAQ output data.
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At the time of El Nino, the trade winds are weaker at the equator over the Pacific ocean why? please explain those who knows exact answer......
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Ravi - Short answer is that the equatorial trade winds are responding to the change in sea surface temperature gradient along the equator. Longer answer: During an El Nino event, the pattern of equatorial sea surface temperatures change. Normally they are cold near South America and warm near Indonesia. During an El Nino, when the warm water moves eastward towards South America (along the thermocline, and reducing the equatorial upwelling), they weaken that gradient, and sometimes event reverse it. As that E-W SST gradient gets weaker, the trade winds get weaker. The intervening atmospheric variable is the sea level pressure.
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Does anyone know about  the impact of ionospheric warming  on the Earth's atmospheric system? 
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As to the question of ionospheric reflection of radio waves affecting the climate, it is clear that no such effect is expected because the radio waves do not interact with the neutral atmosphere and further the energy of the reflected waves is insignificant (as Prof Rastogi pointed out). A more relevant (although equally unlikely) question would be to ask if the radio waves transmissions (that are of higher power) could have any effect on the climate. It is also highly unlikely for the same reason that radio waves do not interact with the neutral atmosphere.
As regards the effect of ionospheric warming on the Earth's atmospheric system, the answer by David Themens is to the point. Further to this I may add that any warming that can occur in the ionosphere is the consequence and not the cause of possible changes in the atmospheric system. 
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Hi all,
I downloaded total precipitation of the ERA INTERIM dataset but the unit is "m" and I'd like to convert to mm/day. Any help is very appreciated.
For example: tp = 0.19181 m => mm/day = ?
Best regards,
Guilherme.
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Depending on which ERA Interim files you have, it will be much more complicated than that.
If you have the high temporal resolution files (i.e. not the daily or monthly averages) you must be careful. If I remember correctly, there is 1 file every 12h for the precipitation fields. That file has the first guess (0h) and also four forecast fields (3h, 6h, 9h, and 12h). Those 5 time-steps, in a single 12h interval file, contain the accumulated precipitation at that time, not the instantaneous nor the 3h average precipitation!
Therefore, if you want to know how much precipitation there was in a day you'll have to add the 12h FCT fields from the two files you have for that day! For example:
  • file for 0z1jan89, fct 3h = precipitation accumulated between 0z1jan89 and 3z1jan89
  • ....
  • file for 0z1jan89, fct 12h = precipitation accum between 0z1jan89 and 12z1jan89
  • file for 12z1jan89, fact12h = precipitation accum between 12z1jan89 and 0z2jan89
  • ...
You can easily realize that ERA Interim precipitation is stored in this strange way by plotting the time series (e.g. for 15 time-steps) from a single grid point. No matter which grid-point you chose, the precipitation only increases from 0z to 12z, then again for 12z to 24z. 
Because this is an accumulated field, its units are in equivalent height (in meters) of the water layer in a 1 m2. Hence, if you take the value at 12z and want to go from accumulated meters to mm/day, you have to multiply by 1000 and divide by 12 (because it was accum between 0z and 12).
Moreover, if you want the 3h averages precipitation rates in mm/h, for instance, you have to calculate the difference between consecutive time-steps in the file. In grads it would be:
set t 2 last
precip_rate = (tp - tp(t-1)) *1000/ 3
Cheers
Henrique
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Though earth's atmosphere exists beyond 50 KM high and up to 100 KM, 99.9% air is only available up to 50 KM height. Water vapor is found only up to 10-12 KM, which factors are not favoring moisture to rise beyond this height?
Weather/Atmospheric/Environmental scientists can answer this question.  
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Yes, and some of the water which reaches the middle & upper stratosphere is broken apart by the solar UV.  This is replaced by the oxidation of methane and molecular hydrogen.  (See Wrotny et al., 2010, J. Geophys. Res., 115, D04302, doi:10.1029/2009JD012135)
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Hi, all
I am trying to simulate storm surge and inland inundation along the region of Bay of Bengal using ADCIRC model . Is there a way to get the required information for preparing the fort.14 and fort. 22 files from open sources or commercial.
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Robert Grumbine
Thank you very much sir
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measuring matter Moisture content commonly conducted by using oven then take the ratio of dry matter and wet matter. this method is so common and accurate enough but takes a long timewhile sometimes we need the value of water content as soon as posible. I ever heard MC meter could measure the water content of a material instantly but i wondering if its accurate enough as The "oven method" and also how this apparatus actually works.
NB:i measure water content of compost raw materials such Paddy straw, Bagasse etc.
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thank you Michael, do you have any recommended references that you think I should read regarding this?
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I would like to know, if monthly rainfall data are missing and can't find any station data to fill it. How can I fill that data?
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PROCEDURE- (An example to estimate annual rainfall)
Statement of missing data problem-
Given the annual precipitation values P1, P2, P3,........,Pat neighbouring 'm' stations 1, 2, 3,.....,m respectively. It is required to find the missing precipitation Px at a station X not included in the above m stations. Further, the normal annual precipitations N1,N2,....  at each of the above m+1 stations, including station X are known. 
METHOD 1: AVERAGING from all other stations.
If the normal annual rainfall at various stations are within about 10% of the normal annual precipitation at station X then a simple arithmetic average procedure is followed to estimate Px .
Px = [P1 + P2 + P3 + ....+ Pm] / m
METHOD 2: NORMAL RATIO METHOD
If the normal annual precipitations vary considerably, then Pis estimated by weighing the precipitation at various stations by the ratios of normal annual precipitations. This method gives Px as,
Px = (Nx / m)  [ (P1 / N1) + (P2 / N2) + .........+ (Pm / Nm) ]
I hope this help you.
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Dear Respected Researchers,
I am developing an atmospheric correction method using Landsat data set and later, will also test on MODIS and VIIRS data sets. Therefore, I am looking for spectral reflectance measurements acquired in-situ using hand-held multi spectrometer. The location and size of area, and temporal frequency of the measurements don't matter. I understand that reflectance measurements are not freely available, and acquired by individual investigators using their limited financial resources. So, it will be highly appreciated and acknowledged if someone provide me surface reflectance measurements acquired in-situ.
Thank you!
Regards,
Bilal 
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Dear Muhammad,
Your query appears to be ambiguous: it is not clear whether you wish to identify sources of satellite reflectance data or providers of field measurements to be compared with remote sensing data you already have.
Arvind provided you with some useful pointers to satellite data, and there are many more options available. On the other hand, it may be that you are actually looking for spectral reflectance measurements acquired in situ. Please clarify this point, as well as the ultimate purpose of collecting such data.
Also, to elicit more relevant comments and suggestions, please specify the location and size of the area of interest (or does it not matter where such data have been collected?), and indicate when, over which period, and at which temporal frequency such remote sensing and field measurements should be available for your purpose.
Lastly, please note that while multispectral reflectance data acquired from satellite platforms are often freely available from major Space Agencies, field measurements may not be: these are obtained by individual investigators, using their limited financial resources. In fact, there is no single or coordinated organization to collect and distribute such data on a global scale because field data are likely to be of interest only to a rather small group of researchers working on that site.
Regards, Michel.
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I have Ozone data from a ground-based station in µg/m3, but I need the Ozone in Dobson Units (DU) to further input in a software. What's the conversion equation for µg/m3 to DU, i.e.
1 µg/m3 = ?? DU
Thanks.
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Hi Majid,
The two units are for different quantities! You can't relate them directly.
DU is a unit for vertically integrated columns: 1 DU = 2.6867 10^20 molecules.m-2. It is a column density. Or using the molar mass of ozone: 1 DU = 2.1415 10^4 µg(O3)/m2.
µg/m3 is a unit used  for concentrations.You have to integrate vertically it order to get to a column. 1 µg/m3 over a layer of 1 km is 4.667 10^-2 DU. Or using more realistic figures for the troposphere 50 µg/m3 over a layer of 10 km is 23.3 DU. 
Note that by convention in the air quality community, 1 µg/m3 = 0.5 ppbv (parts per billion in volume; 1 pbbv = 1 molecule of ozone per 10^9 molecule of air) and not the result of the standard formula which uses the ambient air temperature. And from ppb, you can also integrate vertically if you know the profile density of air.
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I would like to know the feasibility of using the riegl to measure the haze?
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Dear Lu Ling,
Your question is rather cryptic. It sounds like you would like to use a laser scanner from the Riegl company to measure haze. However, this company produces many different instruments, so you should specify which one you would like to use, and preferably explain its main characteristics, especially the operational wavelength.
Then what exactly would you like to measure about haze? Its presence? Its optical thickness? The number distribution, size and shape of the particles? Do you wish to distinguish between water- and mineral-based particles? Do you care about the haze's chemical composition? Or are you perhaps more interested in identifying and characterizing objects that are concealed by haze? What is your ultimate motive or application?
You will need to study the theory of radiation transfer in turbid media, such as clouds, aerosols, fog, etc., as background to your investigation and use of such an instrument. There is an ample scientific literature, including lots of textbooks, articles, presentations and web sites on this vast topic.
Please sharpen your question: this will greatly help colleagues to provide hints and suggestions, and also help you in ensuring you collect comments that are actually relevant to your problem
Regards, Michel.
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A METEOROLOGIST CAN ANSWER THIS QUESTION  
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Ken
Just noticed that you also addressed me in the latest reply
Sure it depends on latitude going from towering cumuli up to 20 km in the south of your country to low stratus at the poles; but why do you ask? as always the questioner does not react himself why he asked it and apparently never read a textbook like the one I mentioned or even searched in Wikipedia on the origin of precipitation
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Hello Everybody!
Can anyone indicate me where to get satellite derived precipitation timeseries data for 150 to present with a good spatial resolution and a scientifically recognized source?
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