Showing papers on "Nowcasting published in 1999"
TL;DR: In this paper, a radar-based calibration technique is described which is applied to the Negri-Adler-Wetzel scheme for nowcasting purposes in the context of a real-time flood warning scheme.
Abstract: The aim is to evaluate the use of infrared satellite precipitation estimates for nowcasting purposes in the context of a real-time flood-warning scheme. A radar-based calibration technique is described which is applied to the Negri–Adler–Wetzel scheme. This procedure employs radar data over a defined calibration area to estimate, for each satellite image, actual rain-rates to be used in the Negri–Adler–Wetzel scheme. Calibrated satellite estimates obtained from this procedure can be used to diagnose areas of precipitation beyond radar range, thus allowing an extension of precipitation nowcasting lead time. Calibrated estimates are compared with radar rainfall measurements and results are discussed for various sizes of integration area. Calibration reduces consistently both bias and variance of the error of the original Negri–Adler–Wetzel estimates, even for integration areas as small as 2000 km2. This indicates the capabilities of the new technique for nowcasting purposes over mediumsized river basins. Copyright © 1999 Royal Meteorological Society
21 citations
01 Jan 1999
TL;DR: In this article, the authors investigate the use of NOAA AVHRR/3 1.6 m m imagery for snow, cloud and sunglint discrimination (Nowcasting SAF).
Abstract: Investigations of NOAA AVHRR/3 1.6 m m imagery for snow, cloud and sunglint discrimination (Nowcasting SAF)
11 citations
01 Jan 1999
TL;DR: In this paper, the forecasting range limits for catchment areas in accordance with urban requirements (1 to 180 km²) and for two different types of rain were investigated in the city of Nancy, Italy.
Abstract: Guided by the European Legislation regarding the Protection of Environment, and facing difficulties linked to rainy weather, managers must adapt the management of the urban sewage system to every rain event. In these circumstances, weather radar seems a precious tool in evaluating the spatial structure of the rain areas and in anticipating the very short-term evolution of precipitation over the urban centre. But the rainfall variability in space and time restricts the forecasting period, this period varying from a few minutes to a few hours. The word "nowcasting" is used but the forecasting range limit is uncertain.This paper concerns the forecasting range limits for catchment areas in accordance with urban requirements (1 to 180 km²) and for two different types of rain. Specific validation criteria have been defined in accordance with the requirements of the operational department in charge of sewage system management in Nancy. The results show that the limits of forecasting in Nancy vary greatly according to the conditions. These limitations have led to consider an adapted sewage system management strategy using radar data. This strategy is based on predefined management scenarios and real time identification of the type of rain event.
10 citations
Abstract: A statistical objective analysis (SOA) scheme was developed to adjust estimates of rainfall accumulation from the WSR-88D in central Oklahoma using rain gauge measurements from the Oklahoma Mesonetwork. Statistical parameters of these rainfall fields were obtained from a time series of 2-km Constant Altitude Plan Position Indicators at 2 km × 2 km resolution for accumulation time intervals of 15, 30, 60, and 120 min. Results document that the Twin Lakes WSR-88D underestimates rainfall rates by 28% on average. In turn, these errors generate large errors in the streamflow simulations performed for the Dry Creek watershed in north-central Oklahoma. Mean normalized expected error variances of the accumulated rainfall over 1–2-h duration were reduced up to 25% by the SOA scheme. These results are discussed in the context of a hydrometeorological forecast system that uses the analyzed rainfall field to adjust rainfall rates for nowcasting (0–3 h), to improve rainfall forecasts (0–12 h) via its assimila...
10 citations
TL;DR: In this article, a modified FSUGSM was developed by applying a reverse cumulus parameterization alorithm to the regular forecast model, which restructures the vertical humidity distribution and constrains the large-scale model's moisture error growth during model integration.
Abstract: This study explores the nowcasting and short-range forecasting (up to 3 days) skills of rainfall over the tropics using a high resolution global model. Since the model-predicted rainfall is very sensitive to model parameters, four key model parameters were first selected. They are the Asselin filter coefficient, the fourth order horizontal diffusion coefficient, the surface moisture flux coefficient, and the vertical diffusion coefficient. The optimal values were defined as those which contributed to the best one day rainfall forecasts in the present study. In order to demonstrate and improve the precipitation forecast skill, several numerical experiments were designed using the 14-level Florida State University Global Spectral Model (FSUGSM) at a resolution of T106. Comparisons were also made of the short-range forecasts obtained from a control experiment subjected to normal mode initialization (NMI) versus experiments based on physical initialization (PI). The latter experiments were integrated using the original FSUGSM and a modified version. This modified FSUGSM was developed here by applying a reverse cumulus parameterization alorithm to the regular forecast model, which restructures the vertical humidity distribution and constrains the large-scale model’s moisture error growth during the model integration. An improved short-range rainfall prediction skill was achieved from the modified FSUGSM in this study. The results showed a better agreement between model-based and observed rainfall intensity and pattern.
8 citations
30 Sep 1999
TL;DR: Nowcast as discussed by the authors is a high-resolution, highly perishable weather hazard information system primarily for the Navy aircraft carrier Battlegroup to improve carrier flight operations, strike capabilities, cruise missile weaponeering, and anti-air warfare in the ship s defense.
Abstract: : ONR provided start-up funding to develop a technical approach for the Nowcast for the Next Generation Navy system (called NOWCAST in this report). The goal is to develop a high-resolution, highly perishable weather hazard information system primarily for the Navy aircraft carrier Battlegroup to improve carrier flight operations, strike capabilities, cruise missile weaponeering, and anti-air warfare in the ship s defense. Designed to be housed in the carrier METOC facility, and in compliance with NITES architecture, NOWCAST will combine or fuse data from a variety of sources to provide the warfighter (end-user) direct access to automatic weather products. These data include three-dimensional winds in the planetary boundary layer, radar imagery from the SPY-1 weather radar obtained from the Aegis cruiser, weather from denied areas obtained from the Weather Web project of the Undersecretary of Defense for Science and Technology, MORIAH data from other ships in the Battlegroup, from-shore and direct readout satellite data, various vertical soundings of the atmosphere, and from-shore numerical weather prediction model output. Products will be web-based and warfighter-defined for easy use to avoid or exploit weather conditions that are pertinent to obtaining a full weather situational awareness of the battlespace environment.
2 citations
01 Jan 1999
TL;DR: The GUST algorithm as mentioned in this paper was developed to forecast wind gusts at the surface only, estimating the maximum magnitude of the vertical velocity through the Vertically Intgrated Liquid water content (VIL) and the Echo Top heights (ET).
Abstract: The GUST algorithm (W) was developed to forecast wind gusts at the surface only. The developed algorithm estimates the maximum magnitude of the vertical velocity through the Vertically Intgrated Liquid water content (VIL) and the Echo Top heights (ET). GUST was evaluated using the wind horizontal divergence fields. Use of this algorithm enabled to document violent winds phenomena in association to thunderstorms in Brazil.
2 citations
22 Jun 1999
1 citations
01 Jan 1999
TL;DR: The Local Data Integration System (LDIS) as discussed by the authors integrates in-situ and remotely-sensed observational data into a series of high-resolution gridded analyses to provide added value for nowcasts and short-ten-n forecasts for two reasons.
Abstract: The Applied Meteorology Unit has configured a Local Data Integration System (LDIS) for east central Florida which assimilates in-situ and remotely-sensed observational data into a series of high-resolution gridded analyses. The ultimate goal for running LDIS is to generate products that may enhance weather nowcasts and short-range (less than 6 h) forecasts issued in support of the 45th Weather Squadron (45 WS), Spaceflight Meteorology Group (SMG), and the Melbourne National Weather Service (NWS MLB) operational requirements. LDIS has the potential to provide added value for nowcasts and short-ten-n forecasts for two reasons. First, it incorporates all data operationally available in east central Florida. Second, it is run at finer spatial and temporal resolutions than current national-scale operational models such as the Rapid Update Cycle and Eta models. LDIS combines all available data to produce grid analyses of primary variables (wind, temperature, etc.) at specified temporal and spatial resolutions. These analyses of primary variables can be used to compute diagnostic quantities such as vorticity and divergence. This paper demonstrates the utility of LDIS over east central Florida for a warm season case study. The evolution of a significant thunderstorm outflow boundary is depicted through horizontal and vertical cross section plots of wind speed, divergence, and circulation. In combination with a suitable visualization too], LDIS may provide users with a more complete and comprehensive understanding of evolving mesoscale weather than could be developed by individually examining the disparate data sets over the same area and time.
TL;DR: An algorithm for storm tracking through weather radar data is presented that relies on the crosscorrelation principle as in TREC (Tracking Radar Echoes by Correlation) and derived algorithms and overcomes some problems highlighted by researchers in previous related studies.
Abstract: An algorithm for storm tracking through weather radar data is presented. It relies on the crosscorrelation principle as in TREC (Tracking Radar Echoes by Correlation) and derived algorithms. The basic idea is to subdivide the radar maps in Cartesian format in a grid of square boxes and to exploit the so called local translation hypothesis. The motion vector is estimated as the space shift such that corresponding boxes at different times exhibit the maximum correlation coefficient. The discussed technique adopts a multiscale, multiresolution, and partially overlapped box grid which adapts to the radar reflectivity pattern. Multiresolution decomposition is performed through 2D wavelet based filtering. Correlation coefficients are calculated taking into account unreliable data (e.g. due to ground clutter or beam shielding) in order to avoid strong undesired motion estimation biases due to the presence of such stationary features. Data are gathered through a C-band multipolarimetric doppler weather radar. Results show that the technique overcomes some problems highlighted by researchers in previous related studies. Comparison with radial velocity maps shows good correlation values; although they may vary depending on the specific event and on the orographic complexity of the considered area, estimated motion fields are consistent with the shift of the pattern determined through simple visual inspection.