Weather and climate forecast services at WRMS

Role of climate and weather in crop production and management

Climate and weather are significant factors affecting agriculture production around the world. Both seasonal and regional variability in weather directly influences crop yield potential. While advances in technology have enabled farmers to adopt modern techniques in agriculture and thus enhance their farm output, weather remains a major factor affecting their crops. From plantation to harvest, precipitation, temperature, sunshine hours and wind can affect the quality and quantity of a crop. The correlation between crop volumes and weather can result in a successful yield or a financial disaster. Extended droughts, heat waves, and ill-timed freezes cause widespread crop losses with massive impacts on regional food production, underlining the importance of weather forecasting in agriculture. Farmers around the world are grappling with fluctuating weather patterns among other factors. As a result, farmers need to be increasingly efficient in their management practices keeping a tab on weather and climate fluctuations.

Weather forecasting methodology

Modern-day weather forecasting is entirely based on numerical weather prediction techniques. The basic idea of numerical weather prediction (NWP) is to sample the state of the fluid at a given time and use the equations of fluid dynamics and thermodynamics to estimate the state of the fluid at some time in the future. The observational inputs for operating these NWP systems are provided by several conventional and nonconventional sources. Country-based weather services provide the surface observations from manually operated and automated weather stations at ground level over land and from weather buoys at sea. The World Meteorological Organization acts to standardize the instrumentation, observing practices and timing of these observations worldwide. Stations either report hourly in METAR reports, or every six hours in SYNOP reports. Sites launch radiosondes, which rise through the depth of the troposphere and well into the stratosphere, which provide the upper air observations TEMP/PILOT containing essential meteorological parameters like temperature, wind, humidity. Data from weather satellites are used in areas where traditional data sources are not available. Compared with similar data from radiosondes, the satellite data has the advantage of global coverage, however at a lower accuracy and resolution. Meteorological radar provides information on precipitation location and intensity, which can be used to estimate precipitation accumulations over time. Additionally, if a pulse Doppler weather radar is used then wind speed and direction can be determined. Commercial flights provide aircraft reports along flying routes, and commercial ships provide ship reports along shipping routes. Research flights using reconnaissance aircraft fly in and around weather systems of interest such as tropical cyclones. Reconnaissance aircraft are also flown over the open oceans during the cold season into systems that cause significant uncertainty in forecast guidance or are expected to be of high impact 3–7 days into the future over the downstream continent.

NWP Models are initialized using this observed data. The irregularly spaced observations are processed by data assimilation and objective analysis methods, which perform quality control and obtain values at locations usable by the model’s mathematical algorithms (usually an evenly spaced grid). The data are then used in the model as the starting point for a forecast. Commonly, the set of equations used to predict the known physics and dynamics of the atmosphere are called primitive equations. These equations are initialized from the analysis data and rates of change are determined. The rates of change predict the state of the atmosphere a short time into the future. The equations are then applied to this new atmospheric state to find new rates of change, and these new rates of change predict the atmosphere at a yet further time into the future. This time stepping procedure is continually repeated until the solution reaches the desired forecast time. The length of the time step is related to the distance between the points on the computational grid.

There are several advanced NWP centers in the world which run their own weather and climate forecast systems. For example one of the prominent centers is US NOAA’s National Center for Environmental Prediction (NCEP) which operates a Global Forecast System (GFS) for short and medium-range weather forecasts and a Climate Forecast System (CFS) for extended range, monthly and seasonal forecasts. As with most works of the U.S. government, GFS/CFS data are not copyrighted and are available for free in the public domain under provisions of U.S. law. Gridded data are available for download through the NOAA National Operational Model Archive and Distribution System (NOMADS). Forecast products and more information on GFS are available at the GFS home page. Because of this, the model serves as the basis for the forecasts of numerous private, commercial and foreign weather companies. Dozens of atmospheric and land-soil variables are available through this dataset, from temperatures, winds, and precipitation to soil moisture and atmospheric ozone concentration.

At WRMS the gridded datasets of GFS and CFS, which are in GRIB2 code form are downloaded. The raw data are subjected to decoding and post-processing procedures through software package developed at WRMS to extract values of several atmospheric parameters of interest, such as temperatures, wind, humidity, precipitation, short and longwave radiation, sunshine hours etc. The post-processed datasets are available at grid points separated by 25 km in GFS and about 100 km in CFS. The grid data are also interpolated to specific station locations using an algorithm. Graphics maps are prepared for synoptic analysis. The data formats are customized for supply to various user groups.

Brief descriptions of the technical aspects of the GFS and CFS are provided below:

  • The NCEP Global Forecast System (GFS)   

             The Global Forecast System (GFS) is a global numerical weather prediction system comprising a 4-dimensional Global Data Assimilation System (GDAS) and a global dynamical forecast model run by the United States’ National Weather Service (NWS).GFS is the operational production suite of numerical weather prediction at the National Centre for Environmental Prediction (NCEP) to provide weather forecasts in short range (0-3 days), medium range (4-10 days) and partly in extended range (beyond 10 days). NCEP’s global forecasts provide deterministic and probabilistic guidance out to 16 days. The forecast model is a spectral model with an equivalent horizontal grid resolution of approximately 13 km for the first 10 days and a comparatively lower resolution of 27 km from 240 to 384 hours (16 days). In the vertical, the model has 64 layers. The GFS model is a coupled model, composed of four separate models (an atmosphere model, an ocean model, a land/soil model, and a sea ice model), which work together to provide an accurate picture of weather conditions. Changes are regularly made to the GFS model to improve its performance and forecast accuracy. It is a constantly evolving and improving weather model. Prior to January 2003, the GFS was known as the GFS Aviation (AVN) model and the GFS Medium Range Forecast (MRF) model. GFS-AVN and MRF products are a collection from NCEP’s NOAAPort. Grids, domains, run frequencies, and output frequencies have changed over the years.The model produces forecast output every hour for the first 120 hours (5 days), and three hourly for day 6 to 10 and 12 hourly from day 11 to 16. The output from the GFS is also used to produce model output statistics. The Global Data Assimilation System uses the maximum amounts of satellite and conventional observations from global sources and generates initial conditions for the global forecasts. The global data assimilation and forecasts are made four times daily at 0000, 0600, 1200 and 1800 UTC. The forecast skill generally decreases with time (as with any numerical weather prediction model) and for longer-term forecasts, only the larger scales retain significant accuracy. It is one of the predominant synoptic scale medium-range models in general use.

    It is our experience that the short and medium range forecasts produced by GFS are skillful and quite useful.

  • Climate Forecast System (CFS) 

            The Climate Forecast System Version 2 (CFSv2)is a fully coupled ocean-land-atmosphere dynamical seasonal prediction system that became operational at NCEP in March 2011. The model represents the global interaction between Earth’s oceans, land, and atmosphere. The system was produced by several dozen scientists under guidance from the National Centers for Environmental Prediction.CFSv2 is the successor of the earlier version of the model named CFS version 1 (CFSv1), which became operational in August 2004. This version has upgrades to nearly all aspects of the data assimilation and forecast model components of the system. The model is used for extended range, long range, and seasonal forecasting.        CFSv1 was the first quasi-global, fully coupled atmosphere-ocean-land model used at NCEP for seasonal prediction. CFSv1 was developed from four independently designed pieces of technology, namely the NCEP/DOE Global Reanalysis, which provided the atmospheric and land surface initial conditions, a global ocean data assimilation system (GODAS) operational at NCEP in 2003, which provided the ocean initial states, NCEP’s Global Forecast System (GFS) operational in 2003 which was the atmospheric model run at a lower resolution of T62L64, and the MOM3 ocean forecast model from GFDL.

            CFSv2 has improvements over CFSv1 in all four components mentioned above, namely the two forecast models and the two data assimilation systems. CFSv2 also has a few novelties: an upgraded four level soil model, an interactive three layer sea-ice model, and prescribed historical (i.e. rising) CO2 concentrations. But above all, CFSv2 was designed to improve consistency between the model states and the initial states produced by the data assimilation system.                                                                                                      The atmospheric component of CFSv2 is the NCEP atmospheric GFS model with significant improvements. GFS is a global spectral model. The atmospheric model has a spectral triangular truncation of 126 waves (T126) in the horizontal (equivalent to nearly a 100 Km grid resolution) and a finite differencing in the vertical with 64 sigma-pressure hybrid layers.CFS uses the latest scientific approaches for taking in or assimilating, observations from data sources including surface observations, upper air balloon observations, aircraft observations, and satellite observations.

    In the operational setting, there are 4 control runs per day from the 0, 6, 12 and 18 UTC cycles of the CFS real-time data assimilation system, out to 9 months. In addition to the control run of 9 months at the 0 UTC cycle, there are 3 additional runs, out to one season. In addition to the control run of 9 months at the 6, 12 and 18 UTC cycles, there are 3 additional runs, out to 45 days. There are a total of 16 CFS runs every day, of which 4 runs go out to 9 months, 3 runs go out to 1 season and 9 runs go out to 45 days. Data sets are freely available on NCEP/CPC web site for downloads. At WRMS the gridded datasets of CFSv2, which are in GRIB2 code form are downloaded, subjected to decoding and post-processing procedures through software package developed at WRMS to extract values of several atmospheric parameters of interest, such as 2m temperature, Tmax, Tmin, mean sea level pressure, humidity, and precipitation rate. This exercise is repeated every 5th day on 1st, 6th, 11th, 16th, 21st and 26th of every month. The individual runs on each day are treated as ensemble members and ensemble averages are worked out. At the end of the last run in the month, a super-ensemble of all the days during that month is computed to obtain monthly forecasts for next nine months. The elements extracted are precipitation, max & min temperatures and humidity. The 45-day forecast daily data are used to work out pentad mean values and anomalies from model climatology. The daily time series of 45-day forecast provides a good idea of the active and break cycles in the monsoon season. Processed forecast information in graphics form is provided on WRMS website Seasonal forecasts of monthly averages are updated at the end of each month; 45-day forecasts are updated every 15 days based on products dated 6th and 21st of each month.