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Real Time Climate Data Comparison Platform - Second Challenge

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Washington DC
In the absence of weather station data, Meteorological Satellite Information (MSI) is crucial to estimating local climate.  MSI is used worldwide to understand climate change effects, formulating strategies for disaster risk management, and analyzing the gap between water supply and demand. This knowledge, especially precipitation behavior – main supplier of water - allows countries to promote effective planning for fighting poverty. However, while MSI can estimate climate where there aren't any weather station, the accuracy of MSI depends heavily on field data (weather stations on the ground) to calibrate outputs. Therefore, there is a contradiction here: if MSI requires field data for accuracy, how can MSI be useful in places where no weather station data is available? The first challenge is to bring together satellite and ground information for users in real time and create a tool which will produce a geospatial representation of both sources of information. The first challenge was already accomplished during the RhoK in Philadelphia. The second challenge is to generate an "averaged" geospatial map from the combination of MSI and the ground data.
Example: 
In the specific case of Bolivia, the National Oceanic and Atmospheric support the Bolivian Government on managing around 34 synoptic stations (Construyendo Herramientas para Evaluar Vulnerabilidades y Estrategias de Adaptación al Cambio Climático en el Sector de Recursos Hídricos de Bolivia, World Bank 2010). As we observe from the figure, there are not stations in the South Western part of the country and in the highest Andeans mountains (close to Titicaca Lake). The report also point out that the Servicio Nacional de Hidrologia y Metereologia (SENAMHI) supplied with 81 stations which had smaller range of timeline information in comparison with these synoptic stations. When MSI from National Aeronautics and Space Administration (NASA) were compared with some weather stations given by SENAMHI, it was observed a discrepancy of mean temperature between 3 to 4 °C. However, when those MSI were compared to the synoptic stations, no significant discrepancies were found. In the case of precipitation, the same discrepancies were concluded when a comparison between MSI and weather stations from SENAMHI was developed. The development of this tool will allow users to have three different perspectives of geospatial meteorological information at real time and decide better in the input quality. Also, it will allow meteorological governmental entities having a more active role in the technology update of their monitoring system. It is important to point out that to successfully implement the real time update of this tool, real time satellite information is required, and therefore support from the NASA experts to supply with real time information will be needed.
Similar Projects and Resources: 

Currently, there are not many studies or projects related with this matter.  After some research, the following webpages and publications were found:

http://weather.rap.ucar.edu/

Hamill, T. M., R. P. d’Entremont, and J. T. Bunting, 1992: A description of the air force real-time nephanalysis model. Wea. Forecasting, 7, 288–307.

Lipschutz, R. C., E. N. Rasmussen, J. K. Smith, J. F. Pratte, and C. R. Windsor, 1989: PROFS’ 1988 real-time Doppler products subsystem. Preprints, 24th Conf. on Radar Meteorology, Tallahassee, FL, Amer. Meteor. Soc., 211–215

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Qualitative Impact: 
More weather data will be available online and quality of imformativon will be improved
Quantitative Impact: 
Researchers, farmers, decision-makers, and public in general - almost the entire population

Comments

Jorge,

 

I've thrown something together for African rainfall data.  See http://map.wepoco.com/fusion.html  click on the map (not on a marker) and wait a while, a plot should appear.  Click on a marker and if there are actual observations available these are shown on the bottom plot.

 

I'm working now on easier to use interfaces to satellite, reanalysis, and forecasts model data.  See http://mike.saunby.net

 

Michael

 

msaunby Nov 20, 2011

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