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Weather Radar (weather + radar)
Selected AbstractsMODIS Biophysical States and NEXRAD Precipitation in a Statistical Evaluation of Antecedent Moisture Condition and Streamflow,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 2 2009B. P. Weissling Abstract:, The potential of remotely sensed time series of biophysical states of landscape to characterize soil moisture condition antecedent to radar estimates of precipitation is assessed in a statistical prediction model of streamflow in a 1,420 km2 watershed in south-central Texas, Moderate Resolution Imaging Spectroradiometer (MODIS) time series biophysical products offer significant opportunities to characterize and quantify hydrologic state variables such as land surface temperature (LST) and vegetation state and status. Together with Next Generation Weather Radar (NEXRAD) precipitation estimates for the period 2002 through 2005, 16 raw and deseasoned time series of LST (day and night), vegetation indices, infrared reflectances, and water stress indices were linearly regressed against observed watershed streamflow on an eight-day aggregated time period. Time offsets of 0 (synchronous with streamflow event), 8, and 16 days (leading streamflow event) were assessed for each of the 16 parameters to evaluate antecedent effects. The model results indicated a reasonable correlation (r2 = 0.67) when precipitation, daytime LST advanced 16 days, and a deseasoned moisture stress index were regressed against log-transformed streamflow. The estimation model was applied to a validation period from January 2006 through March 2007, a period of 12 months of regional drought and base-flow conditions followed by three months of above normal rainfall and a flood event. The model resulted in a Nash-Sutcliffe estimation efficiency (E) of 0.45 for flow series (in log-space) for the full 15-month period, ,0.03 for the 2006 drought condition period, and 0.87 for the 2007 wet condition period. The overall model had a relative volume error of ,32%. The contribution of parameter uncertainties to model discrepancy was evaluated. [source] Using SWAT to Model Streamflow in Two River Basins With Ground and Satellite Precipitation Data,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 1 2009Kenneth J. Tobin Abstract:, Both ground rain gauge and remotely sensed precipitation (Next Generation Weather Radar , NEXRAD Stage III) data have been used to support spatially distributed hydrological modeling. This study is unique in that it utilizes and compares the performance of National Weather Service (NWS) rain gauge, NEXRAD Stage III, and Tropical Rainfall Measurement Mission (TRMM) 3B42 (Version 6) data for the hydrological modeling of the Middle Nueces River Watershed in South Texas and Middle Rio Grande Watershed in South Texas and northern Mexico. The hydrologic model chosen for this study is the Soil and Water Assessment Tool (SWAT), which is a comprehensive, physical-based tool that models watershed hydrology and water quality within stream reaches. Minor adjustments to selected model parameters were applied to make parameter values more realistic based on results from previous studies. In both watersheds, NEXRAD Stage III data yields results with low mass balance error between simulated and actual streamflow (±13%) and high monthly Nash-Sutcliffe efficiency coefficients (NS > 0.60) for both calibration (July 1, 2003 to December 31, 2006) and validation (2007) periods. In the Middle Rio Grande Watershed NEXRAD Stage III data also yield robust daily results (time averaged over a three-day period) with NS values of (0.60-0.88). TRMM 3B42 data generate simulations for the Middle Rio Grande Watershed of variable qualtiy (MBE = +13 to ,16%; NS = 0.38-0.94; RMSE = 0.07-0.65), but greatly overestimates streamflow during the calibration period in the Middle Nueces Watershed. During the calibration period use of NWS rain gauge data does not generate acceptable simulations in both watersheds. Significantly, our study is the first to successfully demonstrate the utility of satellite-estimated precipitation (TRMM 3B42) in supporting hydrologic modeling with SWAT; thereby, potentially extending the realm (between 50°N and 50°S) where remotely sensed precipitation data can support hydrologic modeling outside of regions that have modern, ground-based radar networks (i.e., much of the third world). [source] Very short period quantitative precipitation forecastingATMOSPHERIC SCIENCE LETTERS, Issue 1 2005Neil I. Fox Abstract This article presents an overview of the state of the art of very short period quantitative precipitation forecasting. The authors draw primarily on work presented during the sessions on ,Nowcasting' held at the 6th Symposium on the Hydrological Applications of Weather Radar, in Melbourne, Australia, from 2nd to 4th February 2004, and also include some other work in order to give a more complete picture of the field. Copyright © 2005 Royal Meteorological Society [source] Comparison of calibration methods for the reconstruction of space-time rainfall fields during a rain enhancement experiment in Southern ItalyENVIRONMETRICS, Issue 7 2009Arianna Orasi Abstract The role of rainfall raingauge observations in calibration of radar derived rainfall estimates is investigated. The final goal is the reconstruction of the rainfall fields over the observed area using both information during a rainfall enhancement experiment. Furthermore, we propose a simple protocol to assess the experiment efficacy. A space-time approach and the use of kriging with external drift are applied and compared. Results are again compared with those one obtained through an ordinary kriging (OK). Data come from a dense raingauge network and a weather radar installed in 1992 for the evaluation of a rain enhancement experiment carried out in Southern Italy. In this paper we report detailed results from one seeding operation carried out on 11 April 1992. The procedure to assess the efficacy of rain enhancement experiment is illustrated for 11 seeding operations. Copyright © 2008 John Wiley & Sons, Ltd. [source] Extracting bird migration information from C-band Doppler weather radarsIBIS, Issue 4 2008HANS VAN GASTEREN Although radar has been used in studies of bird migration for 60 years, there is still no network in Europe for comprehensive monitoring of bird migration. Europe has a dense network of military air surveillance radars but most systems are not directly suitable for reliable bird monitoring. Since the early 1990s, Doppler radars and wind profilers have been introduced in meteorology to measure wind. These wind measurements are known to be contaminated with insect and bird echoes. The aim of the present research is to assess how bird migration information can be deduced from meteorological Doppler radar output. We compare the observations on migrating birds using a dedicated X-band bird radar with those using a C-band Doppler weather radar. The observations were collected in the Netherlands, from 1 March to 22 May 2003. In this period, the bird radar showed that densities of more than one bird per km3 are present in 20% of all measurements. Among these measurements, the weather radar correctly recognized 86% of the cases when birds were present; in 38% of the cases with no birds detected by the bird radar, the weather radar claimed bird presence (false positive). The comparison showed that in this study reliable altitudinal density profiles of birds cannot be obtained from the weather radar. However, when integrated over altitude, weather radar reflectivity is correlated with bird radar density. Moreover, bird flight speeds from both radars show good agreement in 78% of cases, and flight direction in 73% of cases. The usefulness of the existing network of weather radars for deducing information on bird migration offers a great opportunity for a European-wide monitoring network of bird migration. [source] A high-resolution radar experiment on the island of JerseyMETEOROLOGICAL APPLICATIONS, Issue 2 2007M. A. Rico-Ramirez Abstract A very high-resolution X-band vertically pointing weather radar was deployed in the island of Jersey, UK, from February to May 2004, to study the variation of the vertical reflectivity of precipitation (VPR) in this region. Intercomparison studies were carried out with a C-band scanning weather radar operated by Jersey Met. Department. C-band radar rainfall estimations and raingauge measurements were well correlated at very short ranges (<10 km), but there are clear difficulties in the estimation of precipitation due to ground clutter, and anomalous propagation echoes as well as the variation of VPR. Average VPRs were obtained from the X-band radar that helped reduce the bright band enhancement in C-band scanning weather radar measurements. This paper presents the overall results obtained during this radar experiment. The experiment was one of a series designed to understand the VPR and design removal algorithms for bright band contamination of quantitative radar measurements of precipitation. Copyright © 2007 Royal Meteorological Society [source] Precipitation analysis using the Advanced Microwave Sounding Unit in support of nowcasting applicationsMETEOROLOGICAL APPLICATIONS, Issue 2 2002Ralf Bennartz We describe a method to remotely sense precipitation and classify its intensity over water, coasts and land surfaces. This method is intended to be used in an operational nowcasting environment. It is based on data obtained from the Advanced Microwave Sounding Unit (AMSU) onboard NOAA-15. Each observation is assigned a probability of belonging to four classes: precipitation-free, risk of precipitation, precipitation between 0.5 and 5 mm/h, and precipitation higher than 5 mm/h. Since the method is designed to work over different surface types, it relies mainly on the scattering signal of precipitation-sized ice particles received at high frequencies. For the calibration and validation of the method we use an eight-month dataset of combined weather radar and AMSU data obtained over the Baltic area. We compare results for the AMSU-B channels at 89 GHz and 150 GHz and find that the high frequency channel at 150 GHz allows for a much better discrimination of different types of precipitation than the 89 GHz channel. While precipitation-free areas, as well as heavily precipitating areas (>5 mm/h), can be identified to high accuracy, the intermediate classes are more ambiguous. This stems from the ambiguity of the passive microwave observations as well as from the non-perfect matching of the different data sources and sub-optimal radar adjustment. In addition to a statistical assessment of the method's accuracy, we present case studies to demonstrate its capabilities to classify different types of precipitation and to work over highly structured, inhomogeneous surfaces. Copyright © 2002 Royal Meteorological Society [source] Extracting bird migration information from C-band Doppler weather radarsIBIS, Issue 4 2008HANS VAN GASTEREN Although radar has been used in studies of bird migration for 60 years, there is still no network in Europe for comprehensive monitoring of bird migration. Europe has a dense network of military air surveillance radars but most systems are not directly suitable for reliable bird monitoring. Since the early 1990s, Doppler radars and wind profilers have been introduced in meteorology to measure wind. These wind measurements are known to be contaminated with insect and bird echoes. The aim of the present research is to assess how bird migration information can be deduced from meteorological Doppler radar output. We compare the observations on migrating birds using a dedicated X-band bird radar with those using a C-band Doppler weather radar. The observations were collected in the Netherlands, from 1 March to 22 May 2003. In this period, the bird radar showed that densities of more than one bird per km3 are present in 20% of all measurements. Among these measurements, the weather radar correctly recognized 86% of the cases when birds were present; in 38% of the cases with no birds detected by the bird radar, the weather radar claimed bird presence (false positive). The comparison showed that in this study reliable altitudinal density profiles of birds cannot be obtained from the weather radar. However, when integrated over altitude, weather radar reflectivity is correlated with bird radar density. Moreover, bird flight speeds from both radars show good agreement in 78% of cases, and flight direction in 73% of cases. The usefulness of the existing network of weather radars for deducing information on bird migration offers a great opportunity for a European-wide monitoring network of bird migration. [source] A gridded hourly precipitation dataset for Switzerland using rain-gauge analysis and radar-based disaggregationINTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 12 2010Marc Wüest Abstract Rain gauges and weather radars both constitute important devices for operational precipitation monitoring. Gauges provide accurate yet spotty precipitation estimates, while radars offer high temporal and spatial resolution yet at a limited absolute accuracy. We propose a simple methodology to combine radar and daily rain-gauge data to build up a precipitation dataset with hourly resolution covering a climatological time period. The methodology starts from a daily precipitation analysis, derived from a dense rain-gauge network. A sequence of hourly radar analyses is then used to disaggregate the daily analyses. The disaggregation is applied such as to retain the daily precipitation totals of the rain-gauge analysis, in order to reduce the impact of quantitative radar biases. Hence, only the radar's advantage in terms of temporal resolution is exploited. In this article the disaggregation method is applied to derive a 15-year gridded precipitation dataset at hourly resolution for Switzerland at a spatial resolution of 2 km. Validation of this dataset indicates that errors in hourly intensity and frequency are lower than 25% on average over the Swiss Plateau. In Alpine valleys, however, errors are typically larger due to shielding effects of the radar and the corresponding underestimation of precipitation periods by the disaggregation. For the flatland areas of the Swiss Plateau, the new dataset offers an interesting quantitative description of high-frequency precipitation variations suitable for climatological analyses of heavy events, the evaluation of numerical weather forecasting models and the calibration/operation of hydrological runoff models. Copyright © 2009 Royal Meteorological Society [source] RAINGAGE NETWORK DESIGN USING NEXRAD PRECIPITATION ESTIMATES,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 5 2002A. Allen Bradley ABSTRACT: A general framework is proposed for using precipitation estimates from NEXRAD weather radars in raingage network design. NEXRAD precipitation products are used to represent space time rainfall fields, which can be sampled by hypothetical raingage networks. A stochastic model is used to simulate gage observations based on the areal average precipitation for radar grid cells. The stochastic model accounts for subgrid variability of precipitation within the cell and gage measurement errors. The approach is ideally suited to raingage network design in regions with strong climatic variations in rainfall where conventional methods are sometimes lacking. A case study example involving the estimation of areal average precipitation for catchments in the Catskill Mountains illustrates the approach. The case study shows how the simulation approach can be used to quantify the effects of gage density, basin size, spatial variation of precipitation, and gage measurement error, on network estimates of areal average precipitation. Although the quality of NEXRAD precipitation products imposes limitations on their use in network design, weather radars can provide valuable information for empirical assessment of rain-gage network estimation errors. Still, the biggest challenge in quantifying estimation errors is understanding subgrid spatial variability. The results from the case study show that the spatial correlation of precipitation at subgrid scales (4 km and less) is difficult to quantify, especially for short sampling durations. Network estimation errors for hourly precipitation are extremely sensitive to the uncertainty in subgrid spatial variability, although for storm total accumulation, they are much less sensitive. [source] | ||||||||||