Climate Data - Science topic

Is it expected that a specific research trend will emerge regarding the issue of climate change that is different from what preceded it, especially after the election of President Donald Trump?

How can I download historical climate data? What are the websites that could be beneficial for predicting rainfall in arid and semi-arid environments characterized by sudden and destructive rainfall?

Have a nice day everyone,

I'm stuck in extracting datasets out of MPI-ESM-MR netcdf files because the latitude ranges from -3*10^6 to 3*10^6 and longitude ranges -5,9*10^6 to 5,9*10^6. Plus, the consecutive latitude and/or longitude values have 50000 between them.

You can see the issue at the screenshot I shared with the question

I'd be so glad if you just tell me how to extract for example data of a point with lat 37.4 and long 27.28.

Thanks in advance

Best regards

I'm developing a machine learning model that requires up-to-date climate data of recent years. However, the historical period in the CMIP6 datasets typically ends in 2014.

Are there any solutions that can provide "historical" climate data extending beyond 2014?

Is it reasonable to use the "SSP 2 RCP 4.5" scenario of 2015-2023 "projection" data as "historical"?

DOGMATISM or ABSURDITY or DOGMABSURDITY ?

( I invented the word "DOGMABSURDITY" for this context. )

Hello, scientific community hope you are doing well. I am modeling the prediction of meteorological drought using the climate data (RCP4.5 & 8.5). However, I got stuck in the bias correction of downloaded data. Could anyone please help me out with this? I have tried to do it in Cmhyd software earlier but did not get the desired results. If anyone has Python code to do it, please share it in DM. Happy modeling. Thanks in advance.

I am working with temperature, precipitation data (0.1 degree from CDS store) and population density data (2.5 min from SEDAC) and often have to change the resolution of either data to achieve a common resolution. I do this using cdo but often get confused on which interpolation method would be more appropriate or give accurate results for a particular variable. And does the choice of method also depends on whether we are interpolating from coarser to finer grid or vice-versa? What method should be used for population data? Can you guide me on this or provide some good references?

cdo remapbil,target_file_name temp.nc outfile.nc cdo remapcon,target_file_name prec.nc outfile.nc

Hello, friends. Currently, I am working on species distribution modelling using maxent. I have run the model using occurrence and climate data from WorldClim. Where i can find calibration area (e.g., buffer zones, minimum convex polygons, enclosing rectangles) biases introduced during the calibration process from my model

Hello, if anyone can help , I am using CMHYD as a tool for bias correction. I use observed data 2006-2019, and cordex downloaded data 1981-2100 . I get No error for the file of observed Nor the evaluation file, but when I click check files I get this error in the terminal

Traceback (most recent call last):

File "climate_swat_plot_ui_org.py", line 1098, in check

File "climate_swat_plot_ui_org.py", line 1160, in pre_process

File "climateArcsSWAT2.pyc", line 1670, in getinnerdata

File "pandas\indexes\base.pyc", line 1264, in __getitem__

IndexError: index 0 is out of bounds for axis 0 with size 0

Anyone can help what is the problem ?

Thanks in advance

I bought climate data from IMD. Now I cannot open the .grd files. I want to use it in GIS. HELP! Aside from grads software, what else can I use to open the file? I tried every possible way so please help me in this.

Let me know if there is a tool or method we can use better than long-term meteorological data to get better yields and determine crop water use.

I have tried with no avail to make a logistic regression and random forest model work.

First i fitted a model with climatic measurements as independent variables (2015-2020, almost five years) and mosquito incidence (coded binary for a n regions) data as target. There were some under-sampling issues that were dealt with and the overall models were significant. Some variables were not-significant (or non important for RF) and not kept in the model.

Then, the same (significant) variables were extracted from a future time climate model for 2080-2085, and used to predict the incidence for said region.

The problem is that it for any year on the future time period it predicts presence for all regions.

Anyone has dealt with problems like this? logistic regression or RF models being inconclusive or unusable when used for prediction?

Best

How can I make extrapolation of weather data of a given area which located near to my study area? (I only have the climate data of that region)

This discussion is to record the maintenance, development, and update of the MATLAB module to detect and analyse marine heatwaves - m_mhw.

Codes-https://github.com/ZijieZhaoMMHW/m_mhw1.0

If you have any suggestions, requests, or questions regarding this toolbox, please feel free to comment or email me

I do not doubt climate change and the disasters that come and will come from it ( ). However, one thing puzzles me: why would melting ice lead to rising ocean water levels? Plain high-school physics tell us it would not: see, for example: https://lnkd.in/d78U9S_f.

Of course, there is another physical thing we've all learnt in high school: warmer substances have a larger volume than cold substances. However, we also know the volume expansion coefficient of water is very tiny: only 210 parts on a million per degree Celsius (https://lnkd.in/e65sxJ5a). Oceans are 3.8 km deep (on average) but span millions of km2, so that can explain a few cm only - at best. Also, studies on rising sea levels in coastal cities show these cities tend to sink. So they need better shore protection but it has got nothing to do with rising ocean levels, it would seem.

Any thoughts, anyone? [Again, I am not a climate change denier. See my rant against John Clauser, for example: https://readingfeynman.org/2023/09/04/another-tainted-nobel-prize/.]

Hi

I intend to model a thermal responde we measured with ERA5 variables, and later, use this model to predict the future responde with CMIP6 variables.

My doubt is, how to use precipitation correctly?

In ERA5 hour analysis total precipitation comes in meters acumulated in 1hour (so its m/h I suppose) and in CMIP6 comes in Kg/m2/s .

Kg/m2/s is the same as mm/s, so I'm wondering if turning ERA5 precipitation from meters/hour to mm/s and use that in a valid way?

Also, am I messing up units considering the grids are not even the same in both datasets?

Thank you!

Cheers, Luís Pereira.

a continuously generated (date based) controlled climate data including temperature, Relative humidity, and solar radiation. A relevant insight or script for a crop growth model

After going to this website https://esgf-node.llnl.gov/search/cmip6/ I am facing doubt in which models to select, since there are many Global Climatic Models available. I am new in this field. Please guide.

how can I obtain free precipitation, temperature, and potential evaporation data for a specific lake for the past 20 years?

Does anyone know how to import climate data into MODFLOW (in ModelMuse)

A growing number of research centers analyzing the planet's climate in the long term, analyzing the progressive process of climate change, developing long-term forecast models of climate change, changes in ocean water temps are publishing the results of their research, which show that as a result of human civilizational activity, as a result of still increasing greenhouse gas emissions, the process of global warming in recent years has accelerated faster than previously predicted just a few years ago. These institutions include, among others, the international research team established at the UN and publishing IPCC reports. In addition to this, the European Space Agency (ESA) Copernicus also recently published the results of its ongoing research on the climate of planet Earth, which showed that in the 1st half of 2023, the average temp. of planet Earth's atmosphere was 16.8 degrees C. This is the highest temp. in the history of measurements. This is further evidence supporting the thesis that the global climate crisis has begun, and that the green economic transformation measures carried out in recent years, including the reduction of greenhouse gas emissions, were definitely insufficient. In addition, in many countries, these actions have fallen far short of the pledges made at the UN Climate COP. Besides, the results of the aforementioned studies also support the thesis that the process of global warming in recent years has accelerated faster than previously predicted. This is a particularly important issue in the context of the living conditions of the next generations of people on the planet. Living conditions will rapidly deteriorate for many people on the planet in the not too distant future. Increasing summer heat, droughts, forest fires, weather anomalies, violent storms, drinking water shortages, deterioration of air quality, melting glaciers, rising water levels in the seas and oceans, shrinking areas of forests and other types of natural ecosystems, deterioration of the natural environment, progressive loss of biodiversity of natural ecosystems, extinction of pollinating insects and many other forms of life, etc. these are the key effects of the progressive global warming process, which will determine the deterioration of the quality of life on the planet for many people. In 2023 and 2024, the El Ninio effect is also an additional factor generating an increase in atmospheric temperature. However, according to the results of studies on the planet's climate, analyses of long-term climate change El Ninio is only an additional factor to the main factor is still the rapidly increasing greenhouse gas emissions generated by human civilization still based largely on the dirty combustion economy. However, there are many more research results also conducted by other institutions and research centers confirming the above theses. Please also provide other results of research conducted on this issue.

Key aspects of the negative effects of the progressive process of global warming and the related necessary acceleration of the processes of green transformation of the economy in order to decarbonize the economy, slow down the process of global warming, protect the climate, biosphere and biodiversity of the natural ecosystems of the planet, I described in the article:

In the following article, I included the results of the research conducted on the connection of the issue of sustainable development, the genesis and meaning of the Sustainable Development Goals, the essence of sustainable development in the context of social, normative, economic, environmental, climate aspects, as well as human rights, etc. The research also addressed the issue of key determinants of human existential security as an element of the concept of sustainable development.

In view of the above, I address the following question to the esteemed community of scientists and researchers:

Please answer,

I invite everyone to join the discussion,

Thank you very much,

The above text is entirely my own work written by me on the basis of my research.

In writing this text I did not use other sources or automatic text generation systems.

Copyright by Dariusz Prokopowicz

On my profile of the Research Gate portal, you can find several publications on the issues of environmental policy, green transformation of the economy, green economics, sustainable economic development, etc. I invite you to scientific cooperation in these issues.

Best wishes,

Dariusz Prokopowicz

AI aids in climate change mitigation through various applications, such as optimizing renewable energy systems, analyzing climate data for predictive modeling, and enabling more efficient resource management, leading to reduced greenhouse gas emissions and more sustainable practices.

Climate Data Operators (CDO)

Are there any open sources where I can access weather details from past 25 years? (Especially India)

can anyone help me in doing Bias correction such as Quantile mapping using climate data in python ?

I found a discrepancy in ERA5-Land precipitation product. The monthly data is in theory the daily mean precipitation in meters per day. The problem is that when computing this from the hourly data the result is not the same value. I computed this for several months and grid-points, and I found in each case a discrepancy. Does anybody know why this discrepancy exists? Is there a problem/issue with the ERA5-Land product? If that is the case, which product is "correct"?

When I found this, looked for it on google and I only found this post: https://stackoverflow.com/questions/75698506/discrepancy-in-data-on-total-precipitation-from-era5-monthly-vs-daily-data

There another person got the same discrepancy, then in the comments, another researcher got also the mismatch in the precipitation values.

I am trying to do bias correction for rainfall data using the ‘qmap’ package in R.

My daily observed data is collected from 1981 to 2014 at the point (station) scale. To downscale the future data, I extracted the variable for corresponding locations from GCMs, including historical and future period (1981-2100).

The observed and GCMs data are detected the relationship and estimate the parameters of downscaling future data. But when I do that using qmap package, the results are poor. The output data is aggregated to monthly scale, and evaluated the performance of different GCMs with root mean squared error (RMSE) and correlation coefficient (r). From the results, I found that the bias correction even decreased the r. Additionally, I also provide the boxplot for month and annual precipitation. The above results are based on the fitQmapRQUANT method in the ‘qmap’ package. I also tried to other methods (fitQmapDIST, fitQmapPTF, fitQmapQUANT, and fitQmapSSPLIN), however, the results are still poor.

Does anyone get such results or I am doing it in the wrong way? How can I apply "qmap" to downscale daily precipitation data with my observed data?

Thanks in advance

Hi everyone,

I'm working on a research project on building performance under climate change scenarios. Does anyone know where can I get the future climate data in .EPW format for both typical and extreme weather files based on the IPCC climate change scenarios AR5 or AR6 in the US region?

Thanks a lot for your time,

With regards,

Ahmad Faiz Khan

I need to merge multiple netCDF files into one.

My species of interest is a marine animal, and my study area is the Mediterranean Sea. All previous occurrence points are within the Mediterranean Sea, and my climate data has been clipped down to the size of my study area. When I run MaxEnt with WORLDCLIM BioClim data, the model output gives me only predicted distribution on LAND (mostly coastlines, some inland areas surrounding the Mediterranean Sea). Why is my model generating land-based occurrence points instead of occurrence points in the Med. Sea if my previous occurrence points and climate data occur in/over a waterbody? How can I fix this issue?

specifically for the region of asia

My question might sound bit silly. But, I am really curious to know what makes two GCMs differnt? We know, GCMs simulate past and future climate based on some assumptions. Regarding future, we can not comment anything, but when the task is to simulate past climate, why does the model output vary from each other? Can someone please elaborate on this with some examples?

I have only Temperature and precipitation data of climate station

I will be thankful for any kind of help to extract from binary files using R programming, Python or MATLAB.

Hello dear colleagues

How I am exploring colling strategies in buildings against overheating impact of climate change in this regard, I wanted to know where can i download RCP 8.5 free which are valid and trust

best regard

Hello, I want a weather generator that produces future climate data from 2022 to 2100, except (SDSM , LARS- WG) weather generator because they produce forecasts up to 2055 and I need to 2100, Can anyone recommend one to me?

I am trying to get the bioclim data for each bioclimatic variable (1-19) per each sampling point that I have. I am able to manipulate the data in R and even get them into a data frame so that I can export them to my main excel data sheet. But when I look at the exported data it seems off. For instance, BIO1 is annual mean temp in celsius, but my locations were assigned values of 94-164. Something here is not adding up, many of the other variables are also quite a bit off from what I would expect. Am I interpreting these values incorrectly (is it not just simply a mean temp?) or when I got the data from worldclim did something go wrong?

How the proxy data (from lacustrine records) are useful to validate climate models for future climate predictions?

Any suggestion would be great.

Thank you

Hello.

I understand how to detrend annual climate data, e.g. temperature, rainfall, and radiation.

However, for long-time series of daily climate data, what should I do? Maybe some software does that?

Hope to hear your opinions.

I am looking for some quality biophysical data products (ocean temperature, salinity, dissolved oxygen, Chl-a etc.), especially in-situ ones, for the water bodies situated near the arctic sea and Antarctica. Profiling Floats, Ice-Borne Observing Systems, Ice/Snow Surface Drifters etc. are some of the systems for acquiring polar ocean datasets. So far, I have come across the following sources:

Are these sources good enough to represent ocean dynamics within the polar circle? I would much appreciate it if you could point out some other sources, whether it is in-situ, satellite or model, for understanding polar ocean dynamics.

I'm doing research for my degree thesis in architecture on the urban heat islands of the city of Naples - Italy.

I'm reclassifying the Land surface temeperature map in gis and I am looking for a method to classify the temperatures on the ground in a precise way, according to the classes that allow me to locate the heat islands.

According to information brought by WorldClim the new CMIP6 spatial data on temperature and precipitation should contain 12 layers per each downloadable file (1 layer per month) and the bio-climatic variables should have 19 layers in total.

However, when downloading the files from the page: https://www.worldclim.org/data/cmip6/cmip6_clim2.5m.html each file comes as a zip-file apparently only containing one geotiff-file. I don't know if each *.tif file could be layered, but haven't heard about it being possible? (Not like NetCDF).

I have used the WinRar for decompressing the zip-files and Surfer v. 15 for processing the *.tif files.

Thanks!

Which Climatic Data is required for Temperature Forecasting by Artificial Neural Networks (ANNs)?

where can i download time series dataset in GRIM format to be run on CLIMEX 4.0 Compare Locations/Years? The user guide said that it is available from climond.org but i could not find it

I had two raster stacks; the first represents the current climate data for 2010 and the second represents the future climate data for 2060. Each raster stack comprises 7 raster layers, including the mean annual temperature and mean annual precipitation etc. Is there any code r for computing distance-based climate change velocity?

Thank you so much

All significant climate change studies have been properly studied and are based on the most current, reliable science. Climate models form a reliable guide to potential climate change. However, which data is best for analyzing a certain area's climatic trend remains unclear. Climate data from weather stations or NASA's Worldwide Energy Resource Prediction (POWER).

Is the current (mid-2022) record low water level in rivers a weather anomaly or another example of a long-term unfavourable trend resulting from accelerating global warming?

As of mid-August 2022, river water levels in several countries in Europe are at a 500-year record low.

It has been reported in the meanstream media that, for example, France's longest river, the Loire, can already be crossed on foot in some places.

Besides, the Loire has never flowed so slowly. The Rhine in Germany, on the other hand, is fast becoming impassable by barges.

In Italy, the water level in the Po is 2 metres lower than usual, with devastating effects on crops.

Is this a weather anomaly or another example of an unfavourable trend realised over a multi-year period as a result of accelerating global warming?

Or is it a combination of various unfavourable factors in 2022, which has resulted in the simultaneous occurrence of weather anomalies and the effects of an accelerating global warming process?

On the one hand, many countries have experienced hotter and hotter summers, more and more hot days in the summer period, increasingly frequent droughts, rivers and lakes drying up, less and less rainfall, etc.

On the other hand, an increase in the scale of forest fires has been reported in many countries. In Poland, from the beginning to the middle of 2022, there have already been as many forest fires as in the whole of the previous 2021.

What is your opinion on this subject?

What is your opinion on this topic?

Please reply,

I invite you all to discuss,

Thank you very much,

Greetings,

Dariusz Prokopowicz

I recently acquired subsets of two climate datasets containing data of monthly mean air temperature for my study area, one based on interpolation of surrounding meteorological stations and the other based on climate models. On both of them, despite wich kind of fit i try to do, the coefficient of determination values are below 0.3. Comparing this data with climate data from a relatively close airport, the values show some linear trend, but coefficient of determination is 0.65 and the slope has an opposite sign. What maybe the reason of this apparent local absence of trends? I thank you in advance for your time and consideration on this matter. (On the graphs: y-axis degree celsius, x-axis years).

I have climate and vegetation data by latitude longitude. The vegetation data is a categorical variable with 20 possible options. The climate data is relative frequency of occurrence (RFO) for a dozen climate states - adding up to 100%.

What I want is a model where I can input the vegetation data classification, and it gives a most likely RFO for each climate state.

Using Random Forest models I already know that the climate data can predict the vege classification ~80% of the time, with quite a lot of 'near-misses' according to the confusion matrix, but what I need is the flip-side of this - if I input the vege data, can I get the RFO of the climate states.

I have been looking at K-means clustering, Linear Discriminant Analysis and Multiple Nominal Logistic Regression, but I am unsure if these apply to what I am trying to achieve. I also thought about producing probability densities for each climate state for each vege type, but I am not sure if there is already a method that would do that in a more functional way.

Are their existing statistical or machine learning techniques that achieve what I am trying to do?

I was wondering if there's any tutorial or scripts that can be used to directly downscale and correct the bias in a reanalysis data available as NetCDF such as those provided by CRU or GPCC?

Also, for the downscaling part, does this mean we're just interpolating to lower mesh or is there an actual algorithm used to downscale the data in CDO?

Thanks in advance!

then the time column is showing as numbers, not as dates or different time steps. Please guide me how i can convert those nummbers into time steps or dates. Furthermore, if i compare the time column then 01-04-1959 08:00 AM is displaying as 519344.

What should be the method for big netcdf files? It will be good if anyone provide me the Python, R or MatLab code.

Can anyone help me in doing such as downscaling using climate data in python?

Hello friends,

I need to calculate such extreme precipitation indices (e.g., R5D, R20, RR1..etc) using R programming or Python. I will be thankful for any kind of help.

note that:

R5D --> the highest 5-day precipitation amount for each year.

R20 --> the annual count of days with precipitation >20 mm.

RR1 --> the annual count of rainy days > 1mm.

Regards,

I need to create a spatial map of precipitation data for a specific basin using python or R. I will be thankful if anyone can help me by sharing python or R code.

hi everyone

I need this dataset to do my thesis.

can you help me?

Hello;

We have two twenty years data sets, for a historical time span, and a future prediction. for both, statistical distributions are fitted for five-year intervals, and for historical and predicted data, the same statistical distribution (Johnson SB, Gen. Pareto, and Wakeby) اave been selected as the most appropriate distributions.

Similar statistical distributions have been obtained for all five-year intervals and for the entire twenty-year time series. We want to know what this similarity in data analysis means?

Best

Saeideh

I have soil moisture dataset of woodland from four different soil depths like 0-5 cm,5-20cm,20-40 cm and 40-70 cm. I also have rainfall, and other climate data for Hydrus 1d model but I don't have any data about root water uptake. please suggest me what should I can do in place of the trees roots parameters. My woodland have Cypress trees.

There are no reliable climate data for my research which is remote and quite far from a weather station. So, NASA power data is the last option. But I need to know the resolution.

Hello Experts,

Since our targeted species is found only in the 2 km region of the study site, we are planning to use 30 m spatial resolution climate data on our Species Distribution Model. But the problem is that my local weather station is capable of providing 20 km resolution data. On the other hand, if I use WorldClim data that is also 1 km.

My questions are

1. Can I use these downscaled data (from 1 km or 20 km) on my local study on SDM, which will be on 30 m resolution?

Thank you for your valuable time.

but i can not get a manual\video for detail analysis  about CDT

How can I get a training manual for CDT? If there are, Please share them with me by email or help me in recommending other tools/material for climate data?

I look forward to your response. Thank you very much again

I am trying to choose models for my Species Distribution Analyses. I know that according to CMIP6, up to 100 models should be released. However, I found only 68 in Meehl et al. 2020. Moreover, there are only five models available to download from the Chelsa database. I wonder how I can prefer one model to another? According to ECS (equilibrium climate sensitivity) or TCR (transient climate response)? If yes, which one is crucial in choosing the model?

How can I measure/predict soil temperature from climate data?

Hi all,

I am trying to look at the relationship between young birds' survival rate and temperature variations. For survival rate estimates, they are in annual resolution e.g. bird survival rate =20% in 2001 and 30% in 2002. While temperature measurements are in monthly resolution e.g. Jan 2001 average temperature = 10degreeC, Feb 2001 average temperature = 15degree C etc.

I tried running two linear regressions between 1) annual average temperature (averaging monthly weather estimates) and annual bird survival rate, 2) monthly average temperature and monthly bird survival rate (copying annual estimates so that all months of each year have the same values).

These two regressions yield very different results. The one using annual weather estimates showed a significant relationship between average temperature and survival rate whereas the monthly one showed no significant association.

I wonder if the difference in result is caused by copying and presenting the annual survival estimates in monthly resolutions? And should I use the annual method or monthly method to get a more accurate result from linear regression?

Thank you

Hi Guys!

I usually download future climate data from Worldclim.org.

Their website says that "Data at 30-seconds spatial resolution is expected to be available by the end of March 2020", however, this has not materialized . . . https://www.worldclim.org/data/cmip6/cmip6climate.html

Does anyone know of alternative sources to download future data at this (1km) resolution?

Many thanks!

Joshua

I am looking for daily/ monthly / annual temperature and precipitation data for southern African Cities and National parks. If there is anyone with a link, please help and also on how to extract it. I tried Worldclim.org/data but cant extract it....is there a special software for this?

Numerous papers talk about creating latilong or lat/long blocks of 1 degree or 2 degrees. Is there a way to do this in ArcGIS? We have nest location data for three species of shorebirds in six states of the US, as well as the location and data for all the weather stations in those six states. We would like to find average temperature during the breeding season in those six states and see if changes in average temperature are correlated with changes in clutch initiation date in the shorebird species. One way we can do this (according to previous studies) is to create latilong blocks and use the data from the weather stations in those blocks where our nests are located. However, we are unfamiliar with how to do this in ArcGIS or if there is another way to do it. Any suggestions are much appreciated!

I am looking for a Matlab toolbox/function for the visualization of climate projection to get the similar figure in the attachment.

Article of the figure:

Can we make ENSEMBLE data of Future temperature and precipitation from CMIP data (different models) available on the WORLDCLIM website?

I mean one dataset representing all the models for different SSP scenarios?

Regards

Gowhar Meraj

Hello everyone,

I am working on a project on maximization of water system performance in a river basin in Chile.

Now, I am looking for streamfow data for hydroelectricity production in the Laja river baisn. Could anyone please suggest a good source to download this data from year 1980 to 2010? Also, I am looking for turbine flow data for the same period.

Thank you in advance.

Regards,

Asmita

When I go to https://www.ncei.noaa.gov/data/cmorph-high-resolution-global-precipitation-estimates/access/30min/8km/?C=S;O=A I have to continue clicking on a year>month>day before I can download something. Who knows how to download all 20 years, and who has a (R?) script to extract it?

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!!!

Alem

I am currently working on climate data for my research. I got daily weather data for minimum temperature, maximum temperature, and precipitation for 35 stations and for 40 years (e.g., 1980-2020). How can I downscaling the data with 5 arc-minute spatial resolution and for different time slices (e.g., 2030-40, 2050-60, 2090-2100)?

Hello all,

I am working on a WEAP model in a mountainous region in Chile. For this, I need data for snow accumulation gauge. The snow water equivalent data [mm] can be downloaded from the nsidc.org website in .hdf format. Can anyone please suggest me a way to convert the .hdf file to a csv file for WEAP? Is there other way to extract SWE in csv format like AppEEARS?

Thank you!

Asmita

I read about CORDEX regional projections but I can't access the data files. Can someone suggest others regional climate models suitable for this area or provide a help on how to access the climate data for CORDEX. Thank you very much!

Hello,

"CDO" is a LINUX based operation for evaluating climate data.

Could any one assists me by means of installing or utilizing CDO on Windows operation systems?

Best regards

Saeideh Baghanian