Questions related to Meteorology
The Heat Wave Phenomenon is seen nowadays almost in all continents of the Planet.
This RG question tries to address the issue in such a way that it could be understood by non-specialists in Meteorology.
Thanks in advance for your interest in this thread question.
After computation of NDVI over a region it has to be correlated with meteorology or hydrological indices. How to fulfill this?
This question delves into the fundamental nature of turbulence, a ubiquitous phenomenon in fluid dynamics that is characterized by chaotic and unpredictable fluid motion. Exploring the mechanisms behind turbulence and finding ways to better understand and predict its behavior is a challenging and active area of research with broad implications in various fields, including engineering, meteorology, and environmental sciences. This question opens up avenues for investigating turbulence models, turbulence control strategies, and the development of advanced computational techniques to simulate and analyze turbulent flows.
A project I am working on is the evaluation of stigma temperature in outdoor conditions (solar radiation up to 800-900 W/m2 and air temperature varying between 10-30 degrees).
I am utilizing three different instruments,
1. Thermal Camera that can be attached to a cell phone (thermal expert Q1)
2. Type T thermocouples 32 AWG (0.008 inches in diameter or 0.3255 mm2)
3. IR thermometer
The instruments were calibrated with a certified digital thermometer.
When all three methods are pooled together, we notice that IR camera and thermocouples have near consistent results while the IR thermometer is nearly systematically cooler than the two other methods (of about 1.5 degrees Celsius). This is odd and difficult to explain. Also, these values for the IR thermometer always make stigma cooler than air, which would not make much physical sense as the stigmas don't have any cooling mechanisms to our knowledge. Consequently, I am wondering if anybody has had any experience with any of these three instruments in order to help me get a better understanding of what could be the issue, but most importantly which instrument is actually the best to measure temperature.
Thank you for your time,
Assume you have lots of data measured by weather stations, but unfortunately their temporal coverage is not sufficient to compute a full 30 years climatology (e.g. over the 1991-2020 period). However, you still want to compute some reference for the station which will allow one to get an idea on how warmer/colder or drier/wetter a certain period (week, month, season, year...) was.
I came up with a cool trick, that I believe someone else in the literature used, although I could not find evidence anywhere. Using reanalysis (ERA5-Land) data on a period where I have coverage from both model and station data I can attempt to build a relationship that links the two: can be something simple from a linear regression to something more complicated like find a SVM model that fits the closest grid points values from the reanalysis to the station data.
Once this "model" is found, I can use it on the reanalysis data of the period 1991-2020 to get, as output, a "fictitious" climatology for the station. This works pretty well for temperature, as it has a clear seasonal cycle and no distinct day-to-day variability, but fails completely to capture the precipitation sudden changes, maybe also due to the fact that reanalysis are hardly capturing local precipitation features.
Does someone have any literature suggestion that could make me improve the model?
I have meteorology datasets of 30 meteorology stations from 1992-2022.
However, there are stations that are missing 20% of the sunshine time.
I have completed these using IDV with the help of Python. Is it an appropriate method?
I have time series data regarding "air temperature" collected from experimental field and in parallel i have also collected the air temperature from local meteorological department for 12 months.
So, right now i am eager to know about relationship between them. Is their any statistical significant difference between them (even in any particular month)?
Please suggest the appropriate statistical test we can perform.
Thanks in advance.
There are several ways to obtain solar irradiance data for a specific location in India. Some of the most common sources of this data include:
- National Renewable Energy Laboratory (NREL) - NREL provides a variety of solar resource data, including solar irradiance data, for locations worldwide. This data can be accessed through the NREL website.
- Indian Meteorological Department (IMD) - IMD provides solar radiation data for various locations in India. The data can be accessed through the IMD website.
- Solar Irradiance Monitoring Stations (SIMS) - SIMS are operated by various government and private organizations in India to measure solar radiation levels in different locations. Contacting the operators of the SIMS in the location you are interested in will provide you with more detailed data.
- Research Institutes - Many research institutes in India conduct research on solar energy and may have access to solar irradiance data for specific locations.
- Satellite Data - A number of satellite-based remote sensing agencies like NASA, EUMETSAT, NOAA, etc provides solar irradiance data for certain location.
It's important to note that the accuracy of the data may vary from source to source, so it's important to carefully evaluate the data before using it for any analysis or decision-making.
Dear colleagues I wonder why when I compute the number of cold days using reanalysis data am getting higher percentage over areas where we know climatologically have warmer temperatures and lower in areas where is known climatologically has cold temperatures?
Please see the attached figure for clarification.
I'm making a nested, high resolution simulation (~300m*300m) of WRF in the polar region. The existing static sea ice data is of low resolution and I want to update this static data with a satellite based high resolution data. I need to update the static field only for the inner domain.
How can I do it?
What are the best tools for the same?
Anything in particular I should be careful about?
Thank you in advance.
Hello, everyone! Do you know some open-source weather generator, which could generate meteorological factors at the daily scale, including precipitation, radiation, wind speed, wind direction, humidity, and temperature.
I will preciate that if you could share it with me.
Do we have a convenient database to get knowledge about mean annual values of precipitates (MAP) and temperatures (MAT) around the world? For example, I have longitude and latitude coordinates. How can I get MAP and MAT values?
Please give an example of a scientific hypothesis about Antarctica that has not yet been confirmed. I am also interested in the gaps in scientific knowledge about Antarctica. As a member of Russian Antarctic program I am familiar with general enigmas (ice sheet stability, life in subglacial lakes, paleoclimatic records, influence of subglacial heat flows etc.). So here I would like you to share with me not so common things about Antarctica
I'm trying to convert netcdf file of daily gridded observational dataset for precipitation to DSS grid file in order to simulate HEC-HMS model. Is there any method to do it, by keeping the spatial distribution of rainfall data?
I have a GRIB1 file in which I need to modify values for several datasets. I create my own np.array and want to replace values for particular dataset in existing file. Does pygrib have the options/possibility for doing that?
I want to conduct rainfall disaggregation using Hyetos package in R. Any one having experience with hyetos in R ? I am confused with how to find the parameters (lambda,phi,kappa,alpha,v,mx,sx) that i need to enter in the function DisagSimul. Is it through the excel sheet or R code ?
Scientists have observed nearly 1 degree Celsius increase of global mean temperature during the last 100 years and they make different predictions / projections on climate change. Has any researcher observed significant change in rainfall amount / pattern in a country or a region during the same period (last 100 years)? If there is so, how you explain that in meteorological / hydrological point of view?
We want to use satellite imagery to find annual meteorological information such as direction charts, frequency and intensity of winds or evaporation rates in an area where there are no meteorological stations.Thank you very much for your help
Many methods in meteorology consider a threshold of grid points that satisfy a certain criterion. I want to select, from daily Era-Interim gridded data, those days in with areas of potential vorticity greater than 20 grid points.
I am interested in high spatial resolution (1-2 km) simulation of surface meteorological variables over the complex mountaineous topography for 2-3 years or more along with historical surface meteorological observations in diverse climatic zones for the past 10 years or more. I would like to develop model and share results.
I'm working on predicting the output power of a power plant using machine learning. I see in multiple articles that they use meteorological parameters like solare irradiation, temperature, wind speed..etc.
I want to know if the power plant parameters affect the output power ? and if there's some article that they use power plant parameters or other parameter that meteorological to predict the output power ?
We are looking for a basic meteorological system with data logger (temp., wind, rain, pressure etc.), solar-powered, suitable for mountain region (1000-2000 m a.s.l.), perhaps with possibility to add some air quality and soil moisture sensors. I would like to hear your experience on which ones you have used and how did it work for your research projects.
I'm going to download meteorological information from the meteorology section of the hysplit software, but I get an error. What should I do to fix this problem?
I´m wondering, whether and where the dust and airborne particles would finally land and be accumulated, like (re-)suspended sediments do in freshwater bodies and oceans? And is there a way to determine the age of the stabilized dust? For reference I would like to mention the following paper which demonstrated a method for the age analysis of sediments.
Thanks for your time and sharing of knowledge.
- Szmytkiewicz, Angelika, and Tamara Zalewska. "Sediment deposition and accumulation rates determined by sediment trap and 210Pb isotope methods in the Outer Puck Bay (Baltic Sea)." Oceanologia 56.1 (2014): 85-106.
i,m need calculate air pollution potential for meteorology station without upper station?
and for describe of climate potential of air pollution need method?
Dear respected RG professionals, I'm doing my climate data with MKT (Mann Kendall Test) and SST (Sens' Slope Test) methods to test TREND and VARIABILITY of the basic climate variables (rainfall and temperature). Can I proceed to do these with climate data of 30 years but only from 5 meteorological stations? What is the minimum number of meteorological stations required for analysis of climate TREND and VARIABILITY?
Thank you in advance for your genuine and helpful answers!!!
I'm now working on the effect of climate change on regional agricultural hydrology processes by using hydrological model. I do know that the CMIP5 data (especailly air temperature, precipitation and et al.) should perform statistical downscaling before driving the model. The NCEP/NCAR and ECMWF (https://cds.climate.copernicus.eu/cdsapp#!/dataset/derived-near-surface-meteorological-variables?tab=overview) reanalysis data have different resolution. Does anyone know the differences, advantages and disadvantages in detail when using the reanalysis data to drive hydrological model ?
The threats that global warming has recently posed to humans in many parts of the world have led us to continue this debate.
So the main question is that what actions need to be taken to reduce the risk of climate warming?
Reducing greenhouse gases now seems an inevitable necessity.
In this part in addition to the aforementioned main question, other specific well-known subjects from previous discussion are revisited. Please support or refute the following arguments in a scientific manner.
% ---------------- *** Updated Discussions of Global Warming (section 1) *** ---------------%
The rate of mean temperature of the earth has been increased almost twice with respect to 60 years ago, it is a fact (Goddard Institute for Space Studies, GISS, data). Still a few questions regarding physical processes associated with global warming remain unanswered or at least need more clarification. So the causes and prediction of this trend are open questions. The most common subjects are listed below:
1) "Greenhouse effect increases temperature of the earth, so we need to diminish emission of CO2 and other air pollutants." The logic behind this reasoning is that the effects of other factors like the sun's activity (solar wind contribution), earth rotation orbit, ocean CO2 uptake, volcanoes activities, etc are not as important as greenhous effect. Is the ocean passive in the aforementioned scenario?
2) Two major physical turbulent fluids, the oceans and the atmosphere, interacting with each other, each of them has different circulation timescale, for the oceans it is from year to millennia that affects heat exchange. It is not in equilibrium with sun instantaneously. For example the North Atlantic Ocean circulation is quasi-periodic with recurrence period of about 7 kyr. So the climate change always has occurred. Does the timescale of crucial players (NAO, AO, oceans, etc) affect the results?
3) Energy of the atmospheric system including absorption and re-emission is about 200 Watt/m2 ; the effect of CO2 is about how many percent to this budget ( 2% or more?), so does it have just a minor effect or not?
4) Climate system is a multi-factor process and there exists a natural modes of temperature variations. How anthropogenic CO2 emissions makes the natural temperature variations out of balance.
6) Some weather and climate models that are based on primitive equations are able to reproduce reliable results. Are the available models able to predict future decadal variability exactly? How much is the uncertainty of the results. An increase in CO2 apparently leads in higher mean temperature value due to radiative transfer.
7) How is global warming related to extreme weather events?
Some of the consequences of global warming are frequent rainfall, heat waves, and cyclones. If we accept global warming as an effect of anthropogenic fossil fuels, how can we stop the increasing trend of temperature anomaly and switching to clean energies?
8) What are the roles of sun activities coupled with Milankovitch cycles?
9) What are the roles of politicians to alarm the danger of global warming? How much are scientists sensitive to these decisions?
10) How much is the CO2’s residence time in the atmosphere? To answer this question precisely, we need to know a good understanding of CO2 cycle.
11) Clean energy reduces toxic buildups and harmful smog in air and water. So, how much building renewable energy generation and demanding for clean energy is urgent?
% ---------------- *** Discussions of Global Warming (section 2) *** ---------------%
Warming of the climate system in the recent decades is unequivocal; nevertheless, in addition to a few scientific articles that show the greenhouse gases and human activity as the main causes of global warming, still the debate is not over and some opponents claim that these effects have minor effects on human life. Some relevant topics/criticisms about global warming, causes, consequences, the UN’s Intergovernmental Panel on Climate Change (IPCC), etc are putting up for discussion and debate:
1) All the greenhouse gases (carbon dioxide, methane, nitrous oxide, chlorofluorocarbons (CFCs), hydro-fluorocarbons, including HCFCs and HFCs, and ozone) account for about a tenth of one percent of the atmosphere. Based on Stefan–Boltzmann law in basic physics, if you consider the earth with the earth's albedo (a measure of the reflectivity of a surface) in a thermal balance, that is: the power radiated from the earth in terms of its temperature = Solar flux at the earth's cross section, you get Te =(1-albedo)^0.25*Ts.*sqrt(Rs/(2*Rse)), where Te (Ts) is temperature at the surface of the earth (Sun), Rs: radius of the Sun, Rse: radius of the earth's orbit around the Sun. This simplified equation shows that Te depends on these four variables: albedo, Ts, Rs, Rse. Just 1% variation in the Sun's activity lead to variation of the earth's surface temperature by about half a degree.
1.1) Is the Sun's surface (photosphere layer) temperature (Ts) constant?
1.2) How much is the uncertainty in measuring the Sun's photosphere layer temperature?
1.3) Is solar irradiance spectrum universal?
1.4) Is the earth's orbit around the sun (Rse) constant?
1.5) Is the radius of the Sun (Rs) constant?
1.6) Is the largeness of albedo mostly because of clouds or the man-made greenhouse gases?
So the sensitivity of global mean temperature to variation of tracer gases is one of the main questions.
2) A favorable climate model essentially is a coupled non-linear chaotic system; that is, it is not appropriate for the long term future prediction of climate states. So which type of models are appropriate?
3) Dramatic temperature oscillations were possible within a human lifetime in the past. So there is nothing to worry about. What is wrong with the scientific method applied to extract temperature oscillations in the past from Greenland ice cores or shifts in types of pollen in lake beds?
4) IPCC Assessment Reports,
IPCC's reports are known as some of the reliable sources of climate change, although some minor shortcomings have been observed in them.
4.1) "What is Wrong With the IPCC? Proposals for a Radical Reform" (Ross McKitrick):
IPCC has provided a few climate-change Assessment Reports during last decades. Is a radical reform of IPCC necessary or we should take all the IPCC alarms seriously? What is wrong with Ross argument? The models that are used by IPCC already captured a few crudest features of climate change.
4.2) The sort of typical issues of IPCC reports:
- The summary reports focus on those findings that support the human interference theory.
- Some arguments are based on this assumption that the models account for most major sources of variation in the global mean temperature anomaly.
- "Correlation does not imply causation", in some Assessment Reports, results gained from correlation method instead of investigating the downstream effects of interventions or a double-blind controlled trial; however, the conclusions are with a level of reported uncertainty.
4.3) Nongovernmental International Panel on Climate Change (NIPCC) also has produced some massive reports to date.
4.4) Is the NIPCC a scientific or a politically biased panel? Can NIPCC climate reports be trusted?
4.5) What is wrong with their scientific methodology?
5) Changes in the earth's surface temperature cause changes in upper level cirrus and consequently radiative balance. So the climate system can increase its cooling processes by these types of feedbacks and adjust to imbalances.
6) What is your opinion about political intervention and its effect upon direction of research budget?
I really appreciate all the researchers who have had active participation with their constructive remarks in these discussion series.
% ---------------- *** Discussions of Global Warming (section 3) *** ---------------%
In this part other specific well-known subjects are revisited. Please support or refute the following arguments in a scientific manner.
1) Still there is no convincing theorem, with a "very low range of uncertainty", to calculate the response of climate system in terms of the averaged global surface temperature anomalies with respect to the total feedback factors and greenhouse gases changes. In the classical formula applied in the models a small variation in positive feedbacks leads to a considerable changes in the response (temperature anomaly) while a big variation in negative feedbacks causes just small variations in the response.
2) NASA satellite data from the years 2000 through 2011 indicate the Earth's atmosphere is allowing far more heat to be emitted into space than computer models have predicted (i.e. Spencer and Braswell, 2011, DOI: 10.3390/rs3081603). Based on this research "the response of the climate system to an imposed radiative imbalance remains the largest source of uncertainty. It is concluded that atmospheric feedback diagnosis of the climate system remains an unsolved problem, due primarily to the inability to distinguish between radiative forcing and radiative feedback in satellite radiative budget observations." So the contribution of greenhouse gases to global warming is exaggerated in the models used by the U.N.’s Intergovernmental Panel on Climate Change (IPCC). What is wrong with this argument?
3) Ocean Acidification
Ocean acidification is one of the consequences of CO2 absorption in the water and a main cause of severe destabilising the entire oceanic food-chain.
4) The IPCC reports which are based on a range of model outputs suffer somehow from a range of uncertainty because the models are not able to implement appropriately a few large scale natural oscillations such as North Atlantic Oscillation, El Nino, Southern ocean oscillation, Arctic Oscillation, Pacific decadal oscillation, deep ocean circulations, Sun's surface temperature, etc. The problem with correlation between historical observations of the global averaged surface temperature anomalies with greenhouse gases forces is that it is not compared with all other natural sources of temperature variability. Nevertheless, IPCC has provided a probability for most statements. How the models can be improved more?
5) If we look at micro-physics of carbon dioxide, theoretically a certain amount of heat can be trapped in it as increased molecular kinetic energy by increasing vibrational and rotational motions of CO2, but nothing prevents it from escaping into space. During a specific relaxation time, the energetic carbon dioxide comes back to its rest statement.
6) As some alarmists claim there exists a scientific consensus among the scientists. Nevertheless, even if this claim is true, asking the scientists to vote on global warming because of human made greenhouse gases sources does not make sense because the scientific issues are not based on the consensus; indeed, appeal to majority/authority fallacy is not a scientific approach.
% ---------------- *** Discussions of Global Warming (section 4) *** ---------------%
In this part in addition to new subjects, I have highlighted some of responses from previous sections for further discussion. Please leave you comments to support/weaken any of the following statements:
1) @Harry ten Brink recapitulated a summary of a proof that CO2 is such an important Greenhouse component/gas. Here is a summary of this argument:
"a) Satellites' instruments measure the radiation coming up from the Earth and Atmosphere.
b) The emission of CO2 at the maximum of the terrestrial radiation at 15 micrometer.
b1. The low amount of this radiation emitted upwards: means that "back-radiation" towards the Earth is high.
b2. Else said the emission is from a high altitude in the atmosphere and with more CO2 the emission is from an even higher altitude where it is cooler. That means that the emission upwards is less. This is called in meteorology a "forcing", because it implies that less radiation /energy is emitted back into space compared to the energy coming in from the sun.
The atmosphere warms so the energy out becomes equals the solar radiation coming in. Summary of the Greenhouse Effect."
At first glance, this reasoning seems plausible. It is based on these assumptions that the contribution of CO2 is not negligible and any other gases like N2O or Ozone has minor effect. The structure of this argument is supported by an article by Schmidt et al., 2010:
By using the Goddard Institute for Space Studies (GISS) ModelE radiation module, the authors claim that "water vapor is the dominant contributor (∼50% of the effect), followed by clouds (∼25%) and then CO2 with ∼20%. All other absorbers play only minor roles. In a doubled CO2 scenario, this allocation is essentially unchanged, even though the magnitude of the total greenhouse effect is significantly larger than the initial radiative forcing, underscoring the importance of feedbacks from water vapour and clouds to climate sensitivity."
The following notions probably will shed light on the aforementioned argument for better understanding the premises:
Q1) Is there any observational data to support the overall upward/downward IR radiation because of CO2?
Q2) How can we separate practically the contribution of water vapor from anthropogenic CO2?
Q3) What are the deficiencies of the (GISS) ModelE radiation module, if any?
Q4) Some facts, causes, data, etc relevant to this argument, which presented by NASA, strongly support this argument (see: https://climate.nasa.gov/evidence/)
Q5) Stebbins et al, (1994) showed that there exists "A STRONG INFRARED RADIATION FROM MOLECULAR NITROGEN IN THE NIGHT SKY" (thanks to @Brendan Godwin for mentioning about this paper). As more than 78% of the dry air contains nitrogen, so the contribution of this element is not negligible too.
2) The mean global temperature is not the best diagnostic to study the sensitivity to global forcing. Because given a change in this mean value, it is almost impossible to attribute it to global forcing. Zonal and meridional distribution of heat flux and temperature are not uniform on the earth, so although the mean temperature value is useful, we need a plausible map of spatial variation of temperature .
3) "The IPCC model outputs show that the equilibrium response of mean temperature to a doubling of CO2 is about 3C while by the other observational approaches this value is less than 1C." (R. Lindzen)
4) What is the role of the thermohaline circulation (THC) in global warming (or the other way around)? It is known that during Heinrich events and Dansgaard‐Oeschger (DO) millennial oscillations, the climate was subject to a number of rapid cooling and warming with a rate much more than what we see in recent decades. In the literature, these events were most probably associated with north-south shifts in convection location of the THC. The formation speed of North Atlantic Deep Water (NADW) affects northerly advection velocity of the warm subtropical waters that would normally heat/cool the atmosphere of Greenland and western Europe.
I really appreciate all the researchers who have participated in this discussion with their useful remarks, particularly Harry ten Brink, Filippo Maria Denaro, Tapan K. Sengupta, Jonathan David Sands, John Joseph Geibel, Aleš Kralj, Brendan Godwin, Ahmed Abdelhameed, Jorge Morales Pedraza, Amarildo de Oliveira Ferraz, Dimitris Poulos, William Sokeland, John M Wheeldon, Michael Brown, Joseph Tham, Paul Reed Hepperly, Frank Berninger, Patrice Poyet, Michael Sidiropoulos, Henrik Rasmus Andersen, and Boris Winterhalter.
I read a paper today regarding trends in drought using SPI values. That got me thinking if there is or can be an index for flooding as well. The issue is that drought is usually a long term meteorological phenomenon while flooding occurs in and for a much shorter time period than drought. So I am now thinking along the lines of the concept of rainy days where any day with a daily rainfall more than 2.5 mm is considered a rainy day. Can a similar concept be applied for flooding as well with maybe rainfall and AMC being the decisive factors? Are there any already established criteria this?
There is a network of weather meteorological stations in a city and I would like to assess the representative ratio of each one. Would you suggest to me a methodology to do this assessment? Thanks
I'm not getting actual/source citation for Indian Meteorological Department (IMD) empirical reduction formula. Who has developed it first?? is it available online for reading??
I'm looking for a quick and reliable way to estimate my missing climatological data. My data is daily and more than 40 years. These data include the minimum and maximum temperature, precipitation, sunshine hours, relative humidity and wind speed. My main problem is the sunshine hours data that has a lot of defects. These defects are diffuse in time series. Sometimes it encompasses several months and even a few years. The number of stations I work on is 18. Given the fact that my data is daily, the number of missing data is high. So I need to estimate missing data before starting work. Your comments and experiences can be very helpful.
Thank you so much for advising me.
My field of expertize is in CFD and not in climatology. But I would start a discussion about the relevance of the numerical methods adopted to solve physical models describing the climate change.
I am interested in details in physical as well as mathematical models and the subsequent numerical solution.
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?
In my study area, there are three different sites I can access meteorological data for the purpose of dispersion using AERMOD. I am however unable to choose between the three and would appreciate it if you would assist me on how to justify the choice for the specific meteorological site to work as a representation of the onsite data
Site-specific weather data is required in order to perform historical simulation of power plants and similar systems. A long record of information is needed to adequately capture the range of operating conditions. An excellent source of such data is the Global Surface Summary of the Day (GSOD) database maintained by the National Climate Data Center (NCDC) operated by the National Oceanographic and Atmospheric Administration (NOAA). Data from thousands of meteorological stations around the world are packaged in "tar balls" (LINUX zip files), one for each year, available at their site ftp.ncdc.noaa.gov/pub/data/gsod/ While these files provide daily values, there is enough information to infer hourly behavior using the method of Waichler and Wigmosta described in, "Development of Hourly Meteorological Values From Daily Data," Proceedings of the American Meteorological Society, 2003.
I want to use climatological ocean circulation data into my numerical model as open boundary condition. I found that HYCOM, SODA or CMEMS do not seem to provide climatological circulation data, and WOA only contains climatological temperature and salinity. Previous method I used is that calculated the mean current velocity by many years Reanalysis data, which is complex and need to download many daily or monthly data. So I wonder is anyone use a climatological ocean current data product? Where can I found? Or why many Reanalysis dataset don't have this?
As it is evident that tropical climate has more variability than sub-tropical climate. Whether this causes difficulties while developing hydrological, climatological, or meteorological models for tropical regions? OR Can we attribute these phenomena as the reason for better models being developed in sub-tropical regions? Kindly comment, please. Thank you!
Prefatory, it may be, because this year the radiations and greenhouse gases interaction feedback processes on different timescale (one of the main factor in monsoon dynamics) which makes the monsoon predictability erratic is not expected to add much uncertainty in the prediction system due to the substantial reduction in the greenhouse gas emissions. Implies, may be an upper hand for potential predictive models in the line. Recall that model ability to predict the SW monsoon is higher with initial conditions been used for the month of Feb., March, April (this years these are main lockdown month in the world when atmosphere is not invaded by atmospheric gases) than months closer to the SW monsoon. On other side, can be also be test bed for the models have near accurate long rage forecasting tendency with early months (as mentioned above) initial conditions.
Over all it may be also be manifested that NATURE can be predicted correctly if it is not disturbed. BUT if we keep on disturbing it then predictability may not be that easy and precise.
If yes, then "Commendations" to the accurate predictability of the monsoon system will be higher this year, I think. Good! This may also considered because of Nature natural tendency is higher this year apart from having well resolved and improved interannual and climate systems predictability aspects in the modelling systems, etc...
Nature is in NATURAL swing. Enjoy and try to be safe! But we should also be ready for the monsoon system predictability in the times to come or years to come when emissions will again be dumped in the earth system. It will certainly obstruct the prediction realities. Consistency is the accuracy in the prediction should be addressed responsibly.
What’s your take on that!
In Poland, we have wind speed data collected from 10 meters according to the WMO (The World Meteorological Organization) standards. In the SWAT manual, the data is accepted for the model from a height of 1.7 meters. Can I convert it to SWAT according to the formula in the manual: uz2 = uz1 * (z2 / z1) ^ 0.2. So when I have wind speed uz1 = 3m / s at 1000cm (10m) then uz2 = 3 * (170/1000) ^ 0.2 = 2.1m / s Is that correct?
As per the Indian Meteorological Department (IMD), for rainy days to be considered, in a day total amount of rainfall should be 2.5mm or more than that. I am curious to know the reason to keep it as 2.5mm not any other value?
What are the impacts associated if we let it be 1 mm instead of 2.5mm?
I need to know weather hydro meteorological variability's have direct impact on wetland degradation. How can I relate the trend of hydro meteorological variables with that of wetland degradation trend? If there is any articles....please share to me..
Except synoptic stations, does anybody know any website/software that gives the climatological data of unequipped places?
I am aware of using interpolation methods, but, I am looking for a method that extract data for a desired location numerically (like an excel format etc,.).
Dear researchers, I want to plot some rainfall trends at different areas of Nepal. Where can I download data for that? Website of Department of Hydrology and Meterology shows that we should buy data from them. I would be very happy to access data freely from any other websites. Thanks.
We have ERA5 time series data which was used to extract maximum and minimum temperature. As the spatial resolution was not good therefore we downscaled it to 90 meters to increase the correlation coefficent value against in-situ data.
so i wanted to know whether this step is scientifically correct or not???
I am from South Peru (Arequipa), I need to make a Raster of Erosivity but I only have climate data like precipitation, some ideas?Does somebody know the procedure? It's for work with InVEST models (Sediment Retention).
From several early research works, I have noticed the negative correlation between temperature and PM2.5. However, in my recent study using Wavelet Coherence (WTC), I found a positive correlation in high-spectrum regions with a delay of approximately one month. What may be the reason behind this? Should we consider the influence of the other meteorological factors?
The meteorological agencies that monitor global climate [NASA, Met Office, NOAA, Japanese Meteorological Agency] calculate temperature anomalies according to data from many stations around the globe.
Where one can find a full list of those stations for each agency?
Do those agencies rely upon the same stations or different?
Are those stations the same every year or different?
There are contradictory research papers about the correlation between meteorological parameters and incidence of COVID-19 in some countries worldwide.
Is there correlation between the meteorological parameters and incidence of COVID-19? Is this differ from country to another?
I want to do some sensitivity analysis by altering the meteorology (e.g. increasing temperature) in WRF-Chem model. Can anybody suggest me how I can do this?
Thanks in advance for your kind help.
I am currently searching for hourly weather data covering the European domain. I have found quite useful the source "ERA5-Land hourly data from 1981 to present", but it seems this is providing climatological data and I probably cannot use them for representing weather.
Do you know whether are there available similar datasets (i.e. with high spatio-temporal resolution) providing data on weather?
Thank you very much for the support.
Why “roadblocks are often been overlooked by forecasters” before moving to the “prediction system (PS)”? Can it (PS) be considered a sustainable in the long term?
Now a days most of the forecasting agencies in India are busy in giving seasonal weather forecast (regional) including extremes and making it instantly available on the net. Many are in race of launching new portal to do so without comprehension of the predictability charade. Mostly been done using numerical modelling systems without exploring (disclosing) the some main factor which are essentially are the roadblocks in predictability.
I think, correcting spatial bias via embedded station data network should not only be the focus, though it will be a help but not sustainable solution. Why main problem lies been often overlooked before moving to PS? For example- intraseasonal variability (main roadblock to the predictability) is not well resolved in GFS forecasting model (or alike other models) and these oftenly used by the forecaster as an input data to their chosen prediction model. My question is, if unresolved or inadequate in specific sense (exam.- not having tendency to reproduce intraseasonal signals) inputs goes into the main predictive model then how sustainable will be the forecast in the long run. I feel, to do any less may result in prediction unsustainable. Surely, it may results in few right prediction and leads to self-acclaimed commendations but in longer run chances of failure in prediction will be higher. In terse, these prediction will have no substantial value in the long term.
For example – in a year when these charade processes will be predominant, forecast will be failure and it leads to socio-economic loss and setback to forecasting organizations. In general it will then, as usual, follow with post-mortem which will again highlights the need in the improvement of microphysics, intraseasonal signals variability, lead lag relationship, issues associated to AWS, standards rules or norms, installations, implementations policies, and money etc. aspects and in some cases probably leads to blame game to defend the failure. Remember, these reasoning to defend the prediction sometimes makes other agency competitive and robust. Healthy criticism can substitute constructiveness. I think, scientific failure must be constructively accepted to explore afresh scientific causes behind instead politicization.
If such things continue then it will be followed with actions such as --- Despondent with exiting forecast, Govt. decided to search for new options, leaving or updating the existing.
I think, Obliviousness should not a substitute for decisive forecasting. Forecasters must ensure that all roadblock are properly addressed or informed properly to tackle forecasting related failures and contingency. The truth must not left to postmortem and implications of the words.
Vaid, B. H.
In Fukushima, a large amount of tritiated water (HTO), which was generated after the nuclear accident caused by the 2011 earthquake, has been stored, and the government is recently considering releasing it after diluting it to 1/7 of the IAEA's international standard concentration.
"Releasing into the ocean is done elsewhere. It's not something new. There is no scandal here," IAEA Director General Rafael Mariano Grossi said in 2021.
As for water isotopes, the global concentration distribution of heavy water (HDO) is well known from infrared observations by meteorological satellites. It is then used for more advanced meteorological analysis.
Potential of Mid‐tropospheric Water Vapor Isotopes to Improve Large‐Scale Circulation and Weather Predictability
My question: Similarly, HTO should be measured by meteorological satellites and its concentration should be controlled on a global scale if it is possible. Please let me know if there are any examples of it being implemented. Or is it technically possible but not yet done? Thank you very much for your attention.
One of the most important conditions for accepting applied agricultural research for international publication is to clarify the meteorological situation during the establishment of experiments, especially in the field, in order to ensure that there are no external factors that have a significant impact on the results of the experiment, and the most important of these factors are meteorological data such as temperature, moisture and others. The conditions for agricultural experiments accepted for publication in the most prestigious agricultural scientific journals may be reviewed Field Crops Research Citscore: 7.4 and Impact Factor: 4.31 On the following link: https://www.journals.elsevier.com/field-crops-research/
Assessing UV exposure is a tedious task, particularly because of the great variability (temporal, spatial, anatomical, etc.). When it comes to individual exposure, dosimetry is the gold standard because of its representativeness. At the population level, the question is more difficult since the implementation costs can be very high and the data processing is complex. The question of using other, more global exposure proxies arises.
One such alternative is the use of satellite data (corrected for the erythemal UV spectrum). We recently published two papers describing recent advances in that direction. The first publication describes the elaboration of a high-resolution 15-year climatology of global UV erythemal irradiance over Switzerland based on satellite data:
Vuilleumier, L., T. Harris, A. Nenes, C. Backes, D. Vernez (2020). Developing a UV climatology for public health purposes using satellite data. Environ. Int., 146, 106177, doi:10.1016/j.envint.2020.106177.
The second publication discusses how this UV climatology can be used to estimate individual UV exposure on specific anatomical zones and makes comparisons with measurements using UV dosimeters mounted on individual subjects. It discusses how satellite-based UV dose estimation can be used to complement UV dosimetry campaign and understand the variability within such campaign studies:
Harris, T. C., L. Vuilleumier, C. Backes, A. Nenes and D. Vernez (2021). Satellite-based personal UV dose estimation. Atmosphere, 12, 268, doi:10.3390/atmos12020268.
Although satellite data represent maximum exposure values because they do not take into account shading effects in particular, they represent an interesting alternative.
Rather than opposing the two techniques, it seems to me that using these two approaches in a complementary way would be judicious. What is your opinion on this issue?
How can I get the correlation between a meteorological observatory between one meteorological station and 10 meteorological stations or more? I’d to calculate this correlation for 1 climatic variable (for example, temperature), or more climatic variables (precipitation, wind speed, solar radiation) for different temporal resolutions (annual, seasonal or monthly). I’m going to calculate this correlation with some software, Excel, Anclim or Rclimdex. What is the best software for my purpose? My goal is to obtain a table that relates different observatories with their correlation for a certain meteorological variable and some temporal resolutions (monthly, seasonal or anual).
When I need to determine an alternative equation for estimating reference evapotranspiration for local with missing data, using meteorological data, from official weather stations, this means that I assume that the humidity is missing and I ignore it and estimate it according to the equations recommended by (FAO Irrigation and Drainage Paper No. 56 Chapter 3) and then determine suitable an alternative equation for estimating reference evapotranspiration, and then also determine the evapotranspiration from the standard equation (FAO Penman-Monteith equation 56) to performance evaluation with an alternative equation.
I also assume for temperature is missing and I ignore it and estimate it according to the equations and determine an alternative equation for estimating reference evapotranspiration, and so on for all climatological parameters.
Or what is the basic rule in determining alternative equations to calculate the reference evapotranspiration in places that are missing data, using meteorological data?
Because all those who define alternative equations for estimating reference evapotranspiration rely on data from agro-meteorological stations.
I would like to create my own TYM2 file to run TRNSYS simulation with my own meteorological data. How should I create my weather data file?
Previous research has suggested an involvement of meteorological conditions in the spread of droplet-mediated viral diseases, such as influenza. However, as for the recent novel coronavirus, few studies have discussed systematically about the role of daily weather in the epidemic transmission of the virus.
Basically, I want to estimate PM2.5 using CNN and LSTM model by considering AOD and Meteorological variables (x1...Xn). In this context, I have decided to train the model by ground-based PM2.5 observation and extracted satellite meteorological variables. After completing the training and testing and validation part(Point-based), I want to apply this model for the spatial prediction of PM2.5. But I am confused about how to I prepare the datasets for this modeling. If anyone gives me technical guidance then I will be highly obliged.
Thank you In advance.
Some people hope that outbreaks of the new coronavirus will wane as temperatures rise, but pandemics often don’t behave in the same way as seasonal outbreaks..