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NASA NTRS2016

Uncertainty Assessment of the NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-GDDP) Dataset

The NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset is comprised of downscaled climate projections that are derived from 21 General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) and across two of the four greenhouse gas emissions scenarios (RCP4.5 and RCP8.5). Each of the climate projections includes daily maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2100 and the spatial resolution is 0.25 degrees (approximately 25 km x 25 km). The GDDP dataset has received warm welcome from the science community in conducting studies of climate change impacts at local to regional scales, but a comprehensive evaluation of its uncertainties is still missing. In this study, we apply the Perfect Model Experiment framework (Dixon et al. 2016) to quantify the key sources of uncertainties from the observational baseline dataset, the downscaling algorithm, and some intrinsic assumptions (e.g., the stationary assumption) inherent to the statistical downscaling techniques. We developed a set of metrics to evaluate downscaling errors resulted from bias-correction ("quantile-mapping"), spatial disaggregation, as well as the temporal-spatial non-stationarity of climate variability. Our results highlight the spatial disaggregation (or interpolation) errors, which dominate the overall uncertainties of the GDDP dataset, especially over heterogeneous and complex terrains (e.g., mountains and coastal area). In comparison, the temporal errors in the GDDP dataset tend to be more constrained. Our results also indicate that the downscaled daily precipitation also has relatively larger uncertainties than the temperature fields, reflecting the rather stochastic nature of precipitation in space. Therefore, our results provide insights in improving statistical downscaling algorithms and products in the future.

climate projection↗

NASA NTRS2016

Uncertainty Assessment of the NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-GDDP) Dataset

The NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset is comprised of downscaled climate projections that are derived from 21 General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) and across two of the four greenhouse gas emissions scenarios (RCP4.5 and RCP8.5). Each of the climate projections includes daily maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2100 and the spatial resolution is 0.25 degrees (approximately 25 km by 25 km). The GDDP dataset has received warm welcome from the science community in conducting studies of climate change impacts at local to regional scales, but a comprehensive evaluation of its uncertainties is still missing. In this study, we apply the Perfect Model Experiment framework (Dixon et al. 2016) to quantify the key sources of uncertainties from the observational baseline dataset, the downscaling algorithm, and some intrinsic assumptions (e.g., the stationary assumption) inherent to the statistical downscaling techniques. We developed a set of metrics to evaluate downscaling errors resulted from bias-correction ("quantile-mapping"), spatial disaggregation, as well as the temporal-spatial non-stationarity of climate variability. Our results highlight the spatial disaggregation (or interpolation) errors, which dominate the overall uncertainties of the GDDP dataset, especially over heterogeneous and complex terrains (e.g., mountains and coastal area). In comparison, the temporal errors in the GDDP dataset tend to be more constrained. Our results also indicate that the downscaled daily precipitation also has relatively larger uncertainties than the temperature fields, reflecting the rather stochastic nature of precipitation in space. Therefore, our results provide insights in improving statistical downscaling algorithms and products in the future.

general circulation model (GCM)↗

NASA NTRS2014

Comparison Of Downscaled CMIP5 Precipitation Datasets For Projecting Changes In Extreme Precipitation In The San Francisco Bay Area.

Water resource managers planning for the adaptation to future events of extreme precipitation now have access to high resolution downscaled daily projections derived from statistical bias correction and constructed analogs. We also show that along the Pacific Coast the Northern Oscillation Index (NOI) is a reliable predictor of storm likelihood, and therefore a predictor of seasonal precipitation totals and likelihood of extremely intense precipitation. Such time series can be used to project intensity duration curves into the future or input into stormwater models. However, few climate projection studies have explored the impact of the type of downscaling method used on the range and uncertainty of predictions for local flood protection studies. Here we present a study of the future climate flood risk at NASA Ames Research Center, located in South Bay Area, by comparing the range of predictions in extreme precipitation events calculated from three sets of time series downscaled from CMIP5 data: 1) the Bias Correction Constructed Analogs method dataset downscaled to a 1/8 degree grid (12km); 2) the Bias Correction Spatial Disaggregation method downscaled to a 1km grid; 3) a statistical model of extreme daily precipitation events and projected NOI from CMIP5 models. In addition, predicted years of extreme precipitation are used to estimate the risk of overtopping of the retention pond located on the site through simulations of the EPA SWMM hydrologic model. Preliminary results indicate that the intensity of extreme precipitation events is expected to increase and flood the NASA Ames retention pond. The results from these estimations will assist flood protection managers in planning for infrastructure adaptations.

Storm↗

NASA NTRSDate not supplied

GPCP Version 3.2 Products and Results

The Global Precipitation Climatology Project (GPCP) products address the need for long-term precipitation products that emphasize homogeneity, following Climate Data Record (CDR) principles. The new-generation Version 3.2 provides key improvements over the operational Version 2.3 such as: finer spatial resolution of 0.5°x0.5°; wider geosynchronous infrared estimation (58°N-S) upgraded with the PERSIANN-CDR algorithm; upgraded retrievals from selected passive microwave sensors (GPROF algorithm) that calibrate the IR input; revised intercalibrations of TOVS and AIRS data (used at high latitudes); climatologies based on CloudSat, TRMM, and GPM to provide overall calibration by modern satellite estimates; the latest Global Precipitation Climatology Centre (GPCC) precipitation gauge analyses over land areas; regional modifications to the gauge undercatch correction; and IMERG half-hourly data input to the Daily V3.2 product. We will show sample analyses that demonstrate aspects of the Version 3.2 precipitation record, such as the global climatology, the time series for global land and ocean total precipitation and snowfall, and the time series of tropical land and ocean daily precipitation rate histograms. For selected analyses we will show improvements in both the Monthly and Daily products in Version 3.2 compared to the operational Version 2.3. In particular, the climatological zonal profile of precipitation in the Southern Ocean, extending south of 40°S, improves a suspected artifact in V2.3. Similarly, the Daily histograms over ocean in Version 3.2 lack the jump in the predecessor Version 1.3 Daily over ocean at the start of 2009, although a smaller jump is introduced in June 2014. The presentation will conclude with a prospectus for the future satellites/sensors and community datasets necessary to continue computation of a consistent CDR product on the one hand, while also potentially contributing to improvements in the historical record.

Global Precipitation Measurement↗

NASA NTRS1994

Production of long-term global water vapor and liquid water data set using ultra-fast methods to assimilate multi-satellite and radiosonde observations

In recent years climate research scientists have recognized the need for increased time and space resolution precipitable and liquid water data sets. This project is designed to meet those needs. Specifically, NASA is funding STC-METSAT to develop a total integrated column and layered precipitable water data set. This is complemented by a total column liquid water data set. These data are global in extent, 1 deg x 1 deg in resolution, with daily grids produced. Precipitable water is measured by a combination of in situ radiosonde observations and satellite derived infrared and microwave retrievals from four satellites. This project combines these data into a coherent merged product for use in global climate research. This report is the Year 2 Annual Report from this NASA-sponsored project and includes progress-to-date on the assigned tasks.

Vonderhaar, Thomas H.↗

NASA NTRS2011

The Precipitation Characteristics of ISCCP Tropical Weather States

We examine the daytime precipitation characteristics of the International Satellite Cloud Climatology Project (ISCCP) weather states in the extended tropics (35 deg S to 35 deg N) for a 10-year period. Our main precipitation data set is the TRMM Multisatellite Precipitation Analysis 3B42 data set, but Global Precipitation Climatology Project daily data are also used for comparison. We find that the most convective weather state (WS1), despite an occurrence frequency below 10%, is the most dominant state with regard to surface precipitation, producing both the largest mean precipitation rates when present and the largest percent contribution to the total precipitation of the tropical zone of our study; yet, even this weather state appears to not precipitate about half the time. WS1 exhibits a modest annual cycle of domain-average precipitation rate, but notable seasonal shifts in its geographic distribution. The precipitation rates of the other weather states tend to be stronger when occurring before or after WS1. The relative contribution of the various weather states to total precipitation is different between ocean and land, with WS1 producing more intense precipitation on average over ocean than land. The results of this study, in addition to advancing our understanding of the current state of tropical precipitation, can serve as a higher order diagnostic test on whether it is distributed realistically among different weather states in atmospheric models.

Lee, Dongmin↗

NASA NTRS2001

Flying the TRMM Satellite in a GCM

By incorporating the Tropical Rainfall Measurement Mission (TRMM) satellite orbital information into the Colorado State University General Circulation Model (CSU GCM), we are able to 'fly' a satellite in the GCM, and sample the simulated atmosphere in the same way as the TRMM sensors sample the real atmosphere. The TRMM-sampled statistics for precipitation and radiative fluxes at annual, intraseasonal, monthly-mean and seasonal-mean diurnal time scales are evaluated by comparing the satellite-sampled against fully-sampled simulated atmospheres. The sampling rates of the TRMM sensors are significantly affected by the sensors' swath widths. The TRMM Microwave Imager (TMI) and the Visible Infrared Scanner (VIRS) sample each 2.25 x 2.25 degree grid box in the Tropics and subtropics about once per day, but at a different local time every day, while the Precipitation Radar (PR) and the Clouds and the Earth's Radiant Energy System (CERES) sensor visit each grid box about once every three days and twice per day, respectively. Besides inadequate samplings resulting from sensors' swath widths, there is a large, systematic diurnal undersampling associated with TRMM's orbital geometry for grid boxes away from the Equator. When only one month of TRMM data are used, this diurnal undersampling can lead to more daytime samples relative to nighttime samples in one hemisphere, and more nighttime samples relative to daytime samples in the other hemisphere. The resulting sampling biases (3-6 W m(exp-2)) are very pronounced in outgoing longwave radiation (OLR) over the subtropical land masses. The sampling errors in OLR monthly- and seasonal-means are less than 8 W m(exp-2) (5%) for each 2.25 x 2.25 degree grid box. The OLR monthly- and seasonal-means are not sensitive to diurnal undersamplings associated with the TRMM orbits and sensors' swath widths. However, this is not the case for total precipitation. Diurnal undersampling could produce errors as large as 20% in the Tropics and 40% in the subtropics, for the zonally averaged monthly-mean rain rates. The TRMM orbits sample each 2.25 x 2.25 degree grid box in the Tropics and subtropics 1-6 times for each hour of the day within a single season. The seasonal-mean diurnal cycles of precipitation and OLR are not well sampled for any one grid box. By either accumulating the satellite data for a long enough period, or averaging the data over a large area with a relatively uniform diurnal signal, the diurnal cycles of precipitation and OLR can be satisfactorily sampled. The effects TRMM sampling errors on the inferred tropical-mean hydrologic cycle and radiative fluxes are also evaluated. There are strong spurious oscillations associated with TRMM's orbital geometry, with periods of 23 days and 3-4 months, in tropical-mean daily and monthly precipitation. While the relative fluctuations of the sampled-OLR are negligible, the relative fluctuations of the sampled precipitation have magnitudes similar to those of the observed climate variability. Caution must therefore be used when applying TRMM observations of tropical-mean precipitation to interpret climate variations at intraseasonal and interannual scales.

Lin, Xin↗

NASA NTRS1985

Passive microwave precipitation measurements for EOS

It is currently planned to mount a Special Sensor Microwave/Imager (SSM/I) on the manned Space Station (MSS) to obtain quantitative global precipitation data. Passive SSM/I instruments could also be included on other satellites, and function in the 19.35 GHz, V/H polarization, 22.35 GHz, V polarization, 37.0, V/H polarization, and 85.5 GHz, V/H polarization modes. Two polar orbiting satellites and the unit on the MSS would be needed to establish a database sufficient for studying monthly, daily and diurnal precipitation variations that are accurate for local regions. Realization of an effective system depends on the development of algorithms for discriminating between rainfall, snow, light rain and ocean background radiation patterns.

Spencer, R. W.↗

NASA NTRS2001

Use of TRMM Data to Test an Improved Parameterization of Stratiform Precipitation

By incorporating the TRMM satellite orbital information into the most recent geodesic version of the Colorado State University General Circulation Model (CSU GCM), we are able to fly a satellite in the GCM, and sample the simulated atmosphere in the same way as the TRMM sensors sample the real atmosphere. The TRMM sampling statistics of precipitation and radiative fluxes at annual, intra-seasonal, monthly-mean and composited diurnal time scales are evaluated by comparing the satellite-sampled against fully-sampled simulated atmospheres. This information provides a valuable guidance for efficient usage of TRMM data and future satellite mission planning. We have also evaluated the effects of TRMM sampling errors on the inferred tropical-mean hydrologic cycle and radiative fluxes. We have found that there are strong spurious oscillations associated with the TRMM orbital geometry, with periods of 23 days and 3-4 months, in tropical-mean daily and monthly precipitation. Caution must therefore be used when applying TRMM observations of tropical-mean precipitation to interpret climate variations at intraseasonal and interannual scales.

Randall, David A.↗

NASA NTRSDate not supplied

High Latitude Considerations in the Latest GPCP monthly and daily products (V3.1)

The Global Precipitation Climatology Project (GPCP) product is a popular combined satellite-gauge precipitation data set in which the long-term standards of consistency and homogeneity is underlined. Here we discuss various high latitude analysis considered in the recently released GPCP V3.1 monthly and daily products. Satellite data are used over land and ocean and obtained from the Special Sensor Microwave Imager (SSMI), Special Sensor Microwave Imager/Sounder (SSMIS), geostationary imagers and polar orbiting infrared sounders. GPCP uses the Global Precipitation Climatology Centre (GPCC) over land, as its in situ component, but prior to combination with satellite data GPCC estimates are adjusted for gauge undercatch. Advanced sensors aboard the Tropical Rainfall Measuring Mission (TRMM), CloudSat, and Global Precipitation Measurement (GPM) mission have enabled more accurate estimation of rain and snowfall rates in recent years. Started with GPCP V3.1 these observations are integrated into GPCP through the development of the Tropical Combined Climatology (TCC) used at lower latitudes and the Merged CloudSat, TRMM, and GPM (MCTG) climatology used over the extra tropics and higher latitudes. Improved calibrations of Television-Infrared Operational Satellite (TIROS) Operational Vertical Sounder (TOVS) and Advanced Infrared Sounder (AIRS) precipitation are used outside 60ºN-S, where inside this zone the Goddard Profiling (GPROF) algorithm retrievals from SSMI/SSMIS is used to calibrate geostationary IR based precipitation estimate at monthly scale. The Gravity Recovery and Climate Experiment (GRACE) mass change observations are used to determine snowfall accumulations over frozen land and arctic basins and to assess gauge undercatch corrections. Observations of snow on sea ice from NASA’s Operation IceBridge (OIB) flights are utilized as an additional tool for snowfall assessment over sea ice. GPCP V3.1 has higher spatial resolution (0.5ox0.5o) than earlier versions (2.5ox2.5o) over both land and ocean, going back to 1983. Version 3 Daily product uses the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) Final Run V06 estimates, where available (initially restricted to 60°N-S), as well as rescaled TOVS/AIRS data in high-latitude areas, all calibrated to the GPCP V3.1 Monthly estimate. GPCP V3.1 shows about 6% increase in global oceanic precipitation and about 4.5% increase over global land and ocean compared to the previous version (V2.3), some major changes occur over the ocean and around 40oS and 60 oS. We will discuss other important changes of GPCP V3.1, compared to the earlier versions, and our future plans. Through this presentation we will also discuss that while ACCP will provide key information about precipitation, synergistic use of other Earth observing systems (e.g., mass change; recognized as a designated mission in 2017 decadal survey) can also help refine precipitation analysis, especially in high latitude and cold regions.

Ali Behrangi↗

NASA NTRS1974

A study of remote sensing as applied to regional and small watersheds. Volume 2: Supporting technical details

The Stanford Watershed Model, the Kentucky Watershed Model and OPSET program, and the NASA-IBM system for simulation and analysis of watersheds are described in terms of their applications to the study of remote sensing of water resources. Specific calibration processes and input and output parameters that are instrumental in the simulations are explained for the following kinds of data: (1) hourly precipitation data; (2) daily discharge data; (3) flood hydrographs; (4) temperature and evaporation data; and (5) snowmelt data arrays. The Sensitivity Analysis Task, which provides a method for evaluation of any of the separate simulation runs in the form of performance indices, is also reported. The method is defined and a summary of results is given which indicates the values obtained in the simulation runs performed for Town Creek, Alabama; Alamosa Creek, Colorado; and Pearl River, Louisiana. The results are shown in tabular and plot graph form. For Vol. 1, see N74-27813.

Source record↗

NASA NTRS1983

Estimates of the seasonal mean vertical velocity fields of the extratropical Northern Hemisphere

Indirect methods are employed to estimate the wintertime and summertime mean vertical velocity fields of the extratropical Northern Hemisphere and intercomparisons are made, together with comparisons with mean seasonal patterns of cloudiness and precipitation. Twice-daily NMC operational analyses produced general circulation statistics for 11 winters and 12 summers, permitting calculation of the seasonal NMC averages for 6 hr forecasts, solution of the omega equation, integration of continuity equation downward from 100 mb, and solution of the thermodynamic energy equation in the absence of diabatic heating. The methods all yielded similar vertical velocity patterns; however, the magnitude of the vertical velocities could not be calculated with great accuracy. Orography was concluded to have less of an effect in summer than in winter, when winds are stronger.

White, G. H.↗

NASA NTRS2010

A Comparison of Satellite Based, Modeled Derived Daily Solar Radiation Data with Observed Data for the Continental US

Many applications of simulation models and related decision support tools for agriculture and natural resource management require daily meteorological data as inputs. Availability and quality of such data, however, often constrain research and decision support activities that require use of these tools. Daily solar radiation (SRAD) data are especially problematic because the instruments require electronic integrators, accurate sensors are expensive, and calibration standards are seldom available. The Prediction Of Worldwide Energy Resources (NASA/POWER; power.larc.nasa.gov) project at the NASA Langley Research Center estimates daily solar radiation based on data that are derived from satellite observations of outgoing visible radiances and atmospheric parameters based upon satellite observations and assimilation models. The solar data are available for a global 1 degree x 1 degree coordinate grid. SRAD can also be estimated based on attenuation of extraterrestrial radiation (Q0) using daily temperature and rainfall data to estimate the optical thickness of the atmosphere. This study compares daily solar radiation data from NASA/POWER (SRADNP) with instrument readings from 295 stations (SRADOB), as well as with values that were estimated with the WGENR solar generator. WGENR was used both with daily temperature and precipitation records from the stations reporting solar data and records from the NOAA Cooperative Observer Program (COOP), thus providing two additional sources of solar data, SRADWG and SRADCO. Values of SRADNP for different grid cells consistently showed higher correlations (typically 0.85 to 0.95) with SRADOB data than did SRADWG or SRADCO for sites within the corresponding cells. Mean values of SRADOB, SRADWG and SRADNP for sites within a grid cell usually were within 1 MJm-2d-1 of each other, but NASA/POWER values averaged 1.1 MJm-2d-1 lower than SRADOB. The magnitude of this bias was greater at lower latitudes and during summer months and may be at least partially explained by assumptions in ambient aerosol properties. Overall, the NASA/POWER solar radiation data are a promising resource for regional modeling studies where realistic accounting of historic variation is required.

White, Jeffrey W.↗

NASA NTRS2010

Regional Model Nesting Within GFS Daily Forecasts Over West Africa

The study uses the RM3, the regional climate model at the Center for Climate Systems Research of Columbia University and the NASA/Goddard Institute for Space Studies (CCSR/GISS). The paper evaluates 30 48-hour RM3 weather forecasts over West Africa during September 2006 made on a 0.5 grid nested within 1 Global Forecast System (GFS) global forecasts. September 2006 was the Special Observing Period #3 of the African Monsoon Multidisciplinary Analysis (AMMA). Archived GFS initial conditions and lateral boundary conditions for the simulations from the US National Weather Service, National Oceanographic and Atmospheric Administration were interpolated four times daily. Results for precipitation forecasts are validated against Tropical Rainfall Measurement Mission (TRMM) satellite estimates and data from the Famine Early Warning System (FEWS), which includes rain gauge measurements, and forecasts of circulation are compared to reanalysis 2. Performance statistics for the precipitation forecasts include bias, root-mean-square errors and spatial correlation coefficients. The nested regional model forecasts are compared to GFS forecasts to gauge whether nesting provides additional realistic information. They are also compared to RM3 simulations driven by reanalysis 2, representing high potential skill forecasts, to gauge the sensitivity of results to lateral boundary conditions. Nested RM3/GFS forecasts generate excessive moisture advection toward West Africa, which in turn causes prodigious amounts of model precipitation. This problem is corrected by empirical adjustments in the preparation of lateral boundary conditions and initial conditions. The resulting modified simulations improve on the GFS precipitation forecasts, achieving time-space correlations with TRMM of 0.77 on the first day and 0.63 on the second day. One realtime RM3/GFS precipitation forecast made at and posted by the African Centre of Meteorological Application for Development (ACMAD) in Niamey, Niger is shown.

Druyan, Leonard M.↗

NASA NTRS2015

Improved Rainfall Estimates and Predictions for 21st Century Drought Early Warning

As temperatures increase, the onset and severity of droughts is likely to become more intense. Improved tools for understanding, monitoring and predicting droughts will be a key component of 21st century climate adaption. The best drought monitoring systems will bring together accurate precipitation estimates with skillful climate and weather forecasts. Such systems combine the predictive power inherent in the current land surface state with the predictive power inherent in low frequency ocean-atmosphere dynamics. To this end, researchers at the Climate Hazards Group (CHG), in collaboration with partners at the USGS and NASA, have developed i) a long (1981-present) quasi-global (50degS-50degN, 180degW-180degE) high resolution (0.05deg) homogenous precipitation data set designed specifically for drought monitoring, ii) tools for understanding and predicting East African boreal spring droughts, and iii) an integrated land surface modeling (LSM) system that combines rainfall observations and predictions to provide effective drought early warning. This talk briefly describes these three components. Component 1: CHIRPS The Climate Hazards group InfraRed Precipitation with Stations (CHIRPS), blends station data with geostationary satellite observations to provide global near real time daily, pentadal and monthly precipitation estimates. We describe the CHIRPS algorithm and compare CHIRPS and other estimates to validation data. The CHIRPS is shown to have high correlation, low systematic errors (bias) and low mean absolute errors. Component 2: Hybrid statistical-dynamic forecast strategies East African droughts have increased in frequency, but become more predictable as Indo- Pacific SST gradients and Walker circulation disruptions intensify. We describe hybrid statistical-dynamic forecast strategies that are far superior to the raw output of coupled forecast models. These forecasts can be translated into probabilities that can be used to generate bootstrapped ensembles describing future climate conditions. Component 3: Assimilation using LSMs CHIRPS rainfall observations (component 1) and bootstrapped forecast ensembles (component 2) can be combined using LSMs to predict soil moisture deficits. We evaluate the skill such a system in East Africa, and demonstrate results for 2013.

Funk, Chris↗

NASA NTRS2015

Improved Rainfall Estimates and Predictions for 21st Century Drought Early Warning

Funk, Chris↗

NASA NTRS2014

Climate Forcing Datasets for Agricultural Modeling: Merged Products for Gap-Filling and Historical Climate Series Estimation

The AgMERRA and AgCFSR climate forcing datasets provide daily, high-resolution, continuous, meteorological series over the 1980-2010 period designed for applications examining the agricultural impacts of climate variability and climate change. These datasets combine daily resolution data from retrospective analyses (the Modern-Era Retrospective Analysis for Research and Applications, MERRA, and the Climate Forecast System Reanalysis, CFSR) with in situ and remotely-sensed observational datasets for temperature, precipitation, and solar radiation, leading to substantial reductions in bias in comparison to a network of 2324 agricultural-region stations from the Hadley Integrated Surface Dataset (HadISD). Results compare favorably against the original reanalyses as well as the leading climate forcing datasets (Princeton, WFD, WFD-EI, and GRASP), and AgMERRA distinguishes itself with substantially improved representation of daily precipitation distributions and extreme events owing to its use of the MERRA-Land dataset. These datasets also peg relative humidity to the maximum temperature time of day, allowing for more accurate representation of the diurnal cycle of near-surface moisture in agricultural models. AgMERRA and AgCFSR enable a number of ongoing investigations in the Agricultural Model Intercomparison and Improvement Project (AgMIP) and related research networks, and may be used to fill gaps in historical observations as well as a basis for the generation of future climate scenarios.

Climate Forcing Data↗

NASA NTRS2001

The Evaluation of the Regional Atmospheric Modeling System in the Eastern Range Dispersion Assessment System

The Applied Meteorology Unit (AMU) evaluated the Regional Atmospheric Modeling System (RAMS) contained within the Eastern Range Dispersion Assessment System (ERDAS). ERDAS provides emergency response guidance for Cape Canaveral Air Force Station and Kennedy Space Center operations in the event of an accidental hazardous material release or aborted vehicle launch. The RAMS prognostic data are available to ERDAS for display and are used to initialize the 45th Space Wing/Range Safety dispersion model. Thus, the accuracy of the dispersion predictions is dependent upon the accuracy of RAMS forecasts. The RAMS evaluation consisted of an objective and subjective component for the 1999 and 2000 Florida warm seasons, and the 1999-2000 cool season. In the objective evaluation, the AMU generated model error statistics at surface and upper-level observational sites, compared RAMS errors to a coarser RAMS grid configuration, and benchmarked RAMS against the nationally-used Eta model. In the subjective evaluation, the AMU compared forecast cold fronts, low-level temperature inversions, and precipitation to observations during the 1999-2000 cool season, verified the development of the RAMS forecast east coast sea breeze during both warm seasons, and examined the RAMS daily thunderstorm initiation and precipitation patterns during the 2000 warm season. This report summarizes the objective and subjective verification for all three seasons.

Case, Jonathan↗