Pierre-Emmanuel Kirstetter

The Catastrophic Flash-Flood Event of 8–9 September 2002 in the Gard Region, France: A First Case Study for the Cévennes–Vivarais Mediterranean Hydrometeorological Observatory

The Cevennes–Vivarais Mediterranean Hydrometeorological Observatory (OHM-CV) is a research initiative aimed at improving the understanding and modeling of the Mediterranean intense rain events that frequently result in devastating flash floods in southern France. A primary objective is to bring together the skills of meteorologists and hydrologists, modelers and instrumentalists, researchers and practitioners, to cope with these rather unpredictable events. In line with previously published flash-flood monographs, the present paper aims at documenting the 8–9 September 2002 catastrophic event, which resulted in 24 casualties and an economic damage evaluated at 1.2 billion euros (i.e., about 1 billion U.S. dollars) in the Gard region, France. A description of the synoptic meteorological situation is first given and shows that no particular precursor indicated the imminence of such an extreme event. Then, radar and rain gauge analyses are used to assess the magnitude of the rain event, which was particularly remarkable for its spatial extent with rain amounts greater than 200 mm in 24 h over 5500 km2. The maximum values of 600–700 mm observed locally are among the highest daily records in the region. The preliminary results of the postevent hydrological investigation show that the hydrologic response of the upstream watersheds of the Gard and Vidourle Rivers is consistent with the marked space–time structure of the rain event. It is noteworthy that peak specific discharges were very high over most of the affected areas (5–10 m3 s−1 km−2) and reached locally extraordinary values of more than 20 m3 s−1 km−2. A preliminary analysis indicates contrasting hydrological behaviors that seem to be related to geomorphological factors, notably the influence of karst in part of the region. An overview of the ongoing meteorological and hydrological research projects devoted to this case study within the OHM-CV is finally presented.

Similarity and difference of the two successive V6 and V7 TRMM multisatellite precipitation analysis performance over China

[1]xa0Similarities and differences of spatial error structures of surface precipitation estimated with successive version 6 (V6) and version 7 (V7) Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) algorithms are systematically analyzed through comparison with the China Meteorological Administrations national daily precipitation analysis from June 2008 to May 2011. The TMPA products include V6 and V7 real-time products 3B42RTV6 and 3B42RTV7 and research products 3B42V6 and 3B42V7. Both versions of research products outperform their respective real-time counterparts. 3B42V7 clearly improves upon 3B42V6 over China in terms of daily mean precipitation; the correlation coefficient (CC) increases from 0.89 to 0.93, the relative bias (RB) improves from −4.91% to −0.05%, and the root-mean-square error (RMSE) improves from 0.69u2009mm to 0.54u2009mm. When considering 3 year mean precipitation, 3B42V7 shows similar spatial patterns and statistical performance to 3B42V6. Both 3B42RTV7 and 3B42RTV6 demonstrate similar bias patterns in most regions of China with overestimation by 20% in arid regions (i.e., the north and west of China) and slight underestimation in humid regions (e.g., −5.82% in southern China). However, 3B42RTV7 overestimates precipitation more than 3B42RTV6 in the cold Qinghai-Tibetan plateau, resulting in a much higher RB of 139.95% (128.69%, 136.09%, and 121.11%) in terms of 3 year annual (spring, summer, and autumn) daily mean precipitation and an even worse performance during winter. In this region, 3B42RTV7 shows an overall slightly degraded performance than 3B42RTV6 with CC decreasing from 0.81 to 0.73 and RB (RMSE) increasing from 21.22% (0.95u2009mm) to 35.84% (1.27u2009mm) in terms of daily precipitation.

Comparing Satellite and Surface Rainfall Products over West Africa at Meteorologically Relevant Scales during the AMMA Campaign Using Error Estimates

Abstract Monsoon rainfall is central to the climate of West Africa, and understanding its variability is a challenge for which satellite rainfall products could be well suited to contribute to. Their quality in this region has received less attention than elsewhere. The focus is set on the scales associated with atmospheric variability, and a meteorological benchmark is set up with ground-based observations from the African Monsoon Multidisciplinary Analysis (AMMA) program. The investigation is performed at various scales of accumulation using four gauge networks. The seasonal cycle is analyzed using 10-day-averaged products, the synoptic-scale variability is analyzed using daily means, and the diurnal cycle of rainfall is analyzed at the seasonal scale using a composite and at the diurnal scale using 3-hourly accumulations. A novel methodology is introduced that accounts for the errors associated with the areal–time rainfall averages. The errors from both satellite and ground rainfall data are computed u...

Evaluation of the successive V6 and V7 TRMM multisatellite precipitation analysis over the Continental United States

[1]xa0The spatial error structure of surface precipitation derived from successive versions of the TRMM Multisatellite Precipitation Analysis (TMPA) algorithms are systematically studied through comparison with the Climate Prediction Center Unified Gauge daily precipitation Analysis (CPCUGA) over the Continental United States (CONUS) for 3 years from June 2008 to May 2011. The TMPA products include the version-6(V6) and version-7(V7) real-time products 3B42RT (3B42RTV6 and 3B42RTV7) and research products 3B42 (3B42V6 and 3B42V7). The evaluation shows that 3B42V7 improves upon 3B42V6 over the CONUS regarding 3 year mean daily precipitation: the correlation coefficient (CC) increases from 0.85 in 3B42V6 to 0.92 in 3B42V7; the relative bias (RB) decreases from −22.95% in 3B42V6 to −2.37% in 3B42V7; and the root mean square error (RMSE) decreases from 0.80 in 3B42V6 to 0.48 mm in 3B42V7. Distinct improvement is notable in the mountainous West especially along the coastal northwest mountainous areas, whereas 3B42V6 (also 3B42RTV6 and 3B42RTV7) largely underestimates: the CC increases from 0.86 in 3B42V6 to 0.89 in 3B42V7, and the RB decreases from −44.17% in 3B42V6 to −25.88% in 3B42V7. Over the CONUS, 3B42RTV7 gained a little improvement over 3B42RTV6 as RB varies from −4.06% in 3B42RTV6 to 0.22% in 3B42RTV7. But there is more overestimation with the RB increasing from 8.18% to 14.92% (0.16–3.22%) over the central US (eastern).

Toward a Framework for Systematic Error Modeling of Spaceborne Precipitation Radar with NOAA/NSSL Ground Radar–Based National Mosaic QPE

Characterization of the error associated with satellite rainfall estimates is a necessary component of deterministic and probabilistic frameworks involving spaceborne passive and active microwave measurements for applications ranging from water budget studies to forecasting natural hazards related to extreme rainfall events. The authors focus here on the error structure of NASA’s Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) quantitative precipitation estimation (QPE) at ground. The problem is addressed by comparison of PR QPEs with reference values derived from ground-based measurements using NOAA/NSSL ground radar‐based National Mosaic and QPE system (NMQ/Q2). A preliminary investigation of this subject has been carried out at the PR estimation scale (instantaneous and 5 km) using a 3-month data sample in the southern part of the United States. The primary contribution of this study is the presentation of the detailed steps required to derive a trustworthy reference rainfall dataset from Q2 at the PR pixel resolution. It relies on a bias correction and a radar quality index, both of which provide a basis to filter out the less trustworthy Q2 values. Several aspects of PR errors are revealed and quantified including sensitivitytothe processing stepswith thereferencerainfall,comparisonsof rainfalldetectabilityandrainfallrate distributions, spatial representativeness of error, and separation of systematic biases and random errors. The methodology and framework developed herein applies more generally to rainfall-rate estimates from other sensors on board low-earth-orbiting satellites such as microwave imagers and dual-wavelength radars such as with the Global Precipitation Measurement (GPM) mission.

Comparison of TRMM 2A25 Products, Version 6 and Version 7, with NOAA/NSSL Ground Radar-Based National Mosaic QPE

Characterization of the error associated with satellite rainfall estimates is a necessary component of deterministic and probabilistic frameworks involving spaceborne passive and active microwave measurements for applications ranging from water budget studies to forecasting natural hazards related to extreme rainfall events. The authors focus here on the relative error structure of Tropical Rainfall Measurement Mission (TRMM) precipitation radar (PR) quantitative precipitation estimation (QPE) at the ground by comparison of 2A25 products with reference values derived from NOAA/NSSL’s ground radar‐based National Mosaic and QPE system (NMQ/Q2). The primary contribution of this study is to compare the new 2A25, version 7 (V7), products that were recently released as a replacement of version 6 (V6). Moreover, the authorssupply uncertainty estimates of the rainfall productsso that they may be used in a quantitative manner for applications like hydrologic modeling. This new version is considered superior over land areas and will likely be the final version for TRMM PR rainfall estimates. Several aspects of the two versions are compared and quantified, including rainfall rate distributions, systematic biases, and random errors. All analyses indicate that V7 is in closer agreement with the reference rainfall compared to V6.

Bollène-2002 Experiment: Radar Quantitative Precipitation Estimation in the Cévennes–Vivarais Region, France

Abstract The Bollene-2002 Experiment was aimed at developing the use of a radar volume-scanning strategy for conducting radar rainfall estimations in the mountainous regions of France. A developmental radar processing system, called Traitements Regionalises et Adaptatifs de Donnees Radar pour l’Hydrologie (Regionalized and Adaptive Radar Data Processing for Hydrological Applications), has been built and several algorithms were specifically produced as part of this project. These algorithms include 1) a clutter identification technique based on the pulse-to-pulse variability of reflectivity Z for noncoherent radar, 2) a coupled procedure for determining a rain partition between convective and widespread rainfall R and the associated normalized vertical profiles of reflectivity, and 3) a method for calculating reflectivity at ground level from reflectivities measured aloft. Several radar processing strategies, including nonadaptive, time-adaptive, and space–time-adaptive variants, have been implemented to a...

Effects of Resolution of Satellite-Based Rainfall Estimates on Hydrologic Modeling Skill at Different Scales

Uncertainty due to resolution of current satellite-based rainfall products is believed to be an important source of error in applications of hydrologic modeling and forecasting systems. A method to account for the input’s resolution and to accurately evaluate the hydrologic utility of satellite rainfall estimates is devised and analyzed herein. A radar-based Multisensor Precipitation Estimator (MPE) rainfall product (4km, 1h) was utilized to assess the impact of resolution of precipitation products on the estimation of rainfall and subsequent simulation of streamflow on a cascade of basins ranging from approximately 500 to 5000km 2 . MPE data were resampled to match the Tropical Rainfall Measuring Mission’s (TRMM) 3B42RT satellite rainfall product resolution (25km, 3h) and compared with its native resolution data to estimate errors in rainfall fields. It was found that resolution degradation considerably modifies the spatial structure of rainfall fields. Additionally, a sensitivity analysis was designed to effectively isolate the error on hydrologic simulations due to rainfall resolution using a distributed hydrologic model. These analyses revealed that resolution degradation introduces a significant amount of error in rainfall fields, which propagated to the streamflow simulations as magnified bias and dampened aggregated error (RMSEs). Furthermore, the scale dependency of errors due to resolution degradation was found to intensify with increasing streamflow magnitudes. The hydrologic model was calibrated with satellite- and original-resolution MPE using a multiscale approach. The resulting simulations had virtually the same skill, suggesting that the effects of rainfall resolution can be accounted for during calibration of hydrologic models, which was further demonstrated with 3B42RT.

Probabilistic precipitation rate estimates with ground‐based radar networks

The uncertainty structure of radar quantitative precipitation estimation (QPE) is largely unknown at fine spatiotemporal scales near the radar measurement scale. By using the WSR-88D radar network and gauge data sets across the conterminous US, an investigation of this subject has been carried out within the framework of the NOAA/NSSL ground radar-based Multi-Radar Multi-Sensor (MRMS) QPE system. A new method is proposed and called PRORATE for probabilistic QPE using radar observations of rate and typology estimates. Probability distributions of precipitation rates are computed instead of deterministic values using a model quantifying the relation between radar reflectivity and the corresponding “true” precipitation. The model acknowledges the uncertainty arising from many factors operative at the radar measurement scale and from the correction algorithm. Ensembles of reflectivity-to-precipitation rate relationships accounting explicitly for precipitation typology were derived at a 5 min/1 km scale. This approach conditions probabilistic quantitative precipitation estimates (PQPE) on the precipitation rate and type. The model components were estimated on the basis of a 1 year long data sample over the CONUS. This PQPE model provides the basis for precipitation probability maps and the generation of radar precipitation ensembles. Maps of the precipitation exceedance probability for specific thresholds (e.g., precipitation return periods) are computed. Precipitation probability maps are accumulated to the hourly time scale and compare favorably to the deterministic QPE. As an essential property of precipitation, the impact of the temporal correlation on the hourly accumulation is examined. This approach to PQPE can readily apply to other systems including space-based passive and active sensor algorithms.

A Unified Flash Flood Database across the United States

Despite flash flooding being one of the most deadly and costly weather-related natural hazards worldwide, individual datasets to characterize them in the United States are hampered by limited documentation and can be difficult to access. This study is the first of its kind to assemble, reprocess, describe, and disseminate a georeferenced U.S. database providing a long-term, detailed characterization of flash flooding in terms of spatiotemporal behavior and specificity of impacts. The database is composed of three primary sources: 1) the entire archive of automated discharge observations from the U.S. Geological Survey that has been reprocessed to describe individual flooding events, 2) flash-flooding reports collected by the National Weather Service from 2006 to the present, and 3) witness reports obtained directly from the public in the Severe Hazards Analysis and Verification Experiment during the summers 2008–10. Each observational data source has limitations; a major asset of the unified flash flood d...