An experimental forecast system based on modern hydrologic models facilitates the evaluation of data assimilation methods, ensemble climate forecasts, and dissemination of visual nowcast and forecast products in ways not possible with current operational methods. H ydrologic extremes are costly to the nation. Annual U.S. drought and flood damages over the last decade have averaged between $6–$8 and $2 billion, respectively (FEMA 1995). Losses associated with the four-year 2000s drought in the western United States are likely to be in the tens of billions of dollars. To the extent that floods and droughts can be mitigated by management of water stored in reservoirs, improved streamflow prediction

project to transition unique observations and research capabilities to the operational community to improve short-term weather forecasts on a regional scale. While the direct beneficiaries of these activities are selected Weather Forecast Offices (WFOs) in the Southern Region, the research leading to the transitional activities benefits the broader scientific community.

) has been developed that is capable of modeling land-atmosphere interactions at spatial resolutions down to 1km. LIS consists of an ensemble of land surface models (e.g., CLM, Noah, VIC) run offline using satellite-based precipitation, radiation and surface parameters, in addition to model-derived surface meteorology. Satellite-based surface parameters include AVHRR-based or MODIS-based land cover and Leaf Area Index (LAI), and MODIS-based albedo and emissivity. The high spatial resolution of LIS, enabled by the use of high performance computing and communications technologies, is capable of resolving mesoscale features, including urban areas, lakes, and agricultural fields. We will present results demonstrating LIS applied at degree, 5km and 1km resolutions. Several demonstration case studies conducted with LIS demonstrate that using LIS has significant advantages for

Abstract—Land surface temperature (LST) is estimated from thermal infrared data provided by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG), using a generalized split-window (GSW) algorithm. The uncertainty of the LST retrievals is highly dependent on the input accuracy and retrieval conditions, particularly the sensor view angle and the atmospheric water vapor content. This paper presents a quantification of the uncertainty of LST estimations, taking into account error statistics of the GSW under a globally representative collection of atmospheric profiles, and a careful characterization of the uncertainty of input data, particularly the surface emissivity and forecasts of the total water vapor content. Such analysis is the basis for LST uncertainty estimation, also distributed to users, in the form of error bars, along with the LST retrievals. Moreover, the spatial coverage of SEVIRI LST is essentially determined by the LST expected uncertainty, instead of being restricted to view zenith angles below a given threshold (e.g., 60 ◦). Within the MSG disk, the atmosphere is often dry for clear-sky conditions where angles are large (e.g., Northern and Eastern Europe and Saudi Arabia). By considering several factors that contribute to LST inaccuracies, it is possible to increase the spatial coverage to regions such as those mentioned earlier. Retrieved values are also compared with in situ observations collected in Namibia, covering a seasonal cycle. The two data sets are in good agreement with root-mean-square differences ranging between 1 ◦ Cand2 ◦ C, which is well below the average error estimated for the satellite retrievals. Index Terms—Infrared measurements, satellite applications, temperature.

Using observation-driven simulations of global terrestrial hydrology and a cluster algorithm that searches for spatially connected regions of soil moisture, the authors identified 296 large-scale drought events (greater than 500 000 km 2 and longer than 3 months) globally for 1950–2000. The drought events were subjected to a severity–area–duration (SAD) analysis to identify and characterize the most severe events for each continent and globally at various durations and spatial extents. An analysis of the variation of large-scale drought with SSTs revealed connections at interannual and possibly decadal time scales. Three metrics of large-scale drought (global average soil moisture, contiguous area in drought, and number of drought events shorter than 2 years) are shown to covary with ENSO SST anomalies. At longer time scales, the number of 12-month and longer duration droughts follows the smoothed variation in northern Pacific and Atlantic SSTs. Globally, the mid-1950s showed the highest drought activity and the mid-1970s to mid-1980s the lowest activity. This physically based and probabilistic approach confirms well-known droughts, such as the 1980s in the Sahel region of Africa, but also reveals many severe droughts (e.g., at high latitudes and early in the time period)

This study evaluates the parameterization of the dependence of surface albedo on solar zenith angle (SZA) over snow-free land surface used by the National Centers for Environmental Prediction Global Forecast Systems (GFS) and those derived from the moderate resolution imaging spectroradiometer satellite observations as described in two recent studies. The influence of cloud cover on surface albedo is also examined. The intensive ground observations of surface shortwave fluxes and cloud covers made by the Atmospheric Radiation Measurement (ARM) Program at the United States Southern Great Plains (SGP) site and at the Tropical Western Pacific (TWP) locale’s Manus and Nauru Islands in 1997 – 2004 are used for the investigation. The albedos for the direct and diffuse solar beams are considered separately. The diffuse-beam albedo is first derived from a subset of the ARM Program samples under overcast conditions and with the downward total flux being all diffuse. Then, it is used to divide the measured surface upward flux into two parts, one associated with the downward direct beam and the other with the downward diffuse beam for all samples. The direct-beam albedo is finally obtained using the first part of the upward flux and the downward direct-beam flux. It is further normalized either by the diffuse-beam albedo or by the direct-beam albedo at SZA=60°. The normalized albedo only varies

Two types of multi-sensor, real-time, 4-km, hourly/6-hourly national precipitation analyses are produced at NCEP and archived at NCAR as part of the GEWEX/GAPP effort: the more

NWS programs, especially those recommending the development of a strategic science plan for hydrologic research at the NWS. Furthermore, the Bulletin of the American Meteorological Society (BAMS) article by Welles et al. (2007) highlights the need for hydrologic forecast verification, and how this verification should be a driving force for new model development. A main section of the document is dedicated to the verification topic. This document is intended to be a “living document ” to be updated on an annual basis. The Plan follows closely the outline of the Integrated Water Science Team report. In each section the Plan offers several subsections: where we are, in which it details what the state of the science at OHD is; where we want to be, where the Plan sets the new directions or reaffirms existing directions; what are the challenges to get there, and a road map to reach those goals. The highlights of the Plan are: The Plan directs the research on hydrologic modeling seeking: A more expeditious and cost-effective approach by reducing the effort required

Abstract not found

Meteorological forcing data are necessary to drive many of the spatial models used to simulate atmospheric, biological, and hydrological processes. Unfortunately, many domains lack sufficient meteorological data and available point observations are not always suitable or reliable for landscape or regional applications. NOAA’s Local Analysis and Prediction System (LAPS) is a meteorological assimilation tool that employs available observations (meteorological networks, radar, satellite, soundings, and aircraft) to generate a spatially distributed, three-dimensional representation of atmospheric features and processes. As with any diagnostic representation, it is important to ascertain how LAPS outputs deviate from a variety of independent observations. A number of surface observations exist that are not used in the LAPS system, and they were employed to assess LAPS surface state variable and precipitation analysis performance during two consecutive years (1 September 2001–31 August 2003). LAPS assimilations accurately depicted temperature and relative humidity values. The ability of LAPS to represent wind speed was satisfactory overall, but accuracy declined with increasing elevation. Last, precipitation estimates performed by LAPS were irregular and reflected inherent difficulties in measuring and estimating precipitation. 1.