Using NWP Analysis in Satellite Rainfall Estimation of Heavy Precipitation Events over Complex Terrain

This study investigates the use of Weather Research and Forecasting (WRF) high-resolution storm analysis in satellite rainfall estimation over complex terrains. Rainfall estimation here is based on the NOAA-Climate Prediction Center morphing (CMORPH) product. Specifically, CMORPH rainfall is adjusted by applying a power-law function whose parameter values are obtained from the comparison between WRF and CMORPH hourly rain rates. Results are presented based on the analyses of five storm cases that induced catastrophic floods in southern Europe. The WRF-based adjusted CMORPH rain rates exhibited improved error statistics against independent radar-rainfall estimates. We show that the adjustment reduces the underestimation of high rain rates thus moderating the strong rainfall magnitude dependence of CMORPH bias. The higher Heidke skill scores for all rain rate thresholds indicate that the adjustment procedure meliorates CMORPH rain rates to provide a better estimation. Results also indicate that the missed rain detection of CMORPH rainfall estimates are also identifiable in the WRF-CMORPH comparison, however, the herein adjustment procedure does not incorporate this effect on CMORPH estimates

Assimilation of Chinese Doppler Radar and Lightning Data Using WRF-GSI: A Case Study of Mesoscale Convective System

The radar-enhanced GSI (version 3.1) system and the WRF-ARW (version 3.4.1) model were modified to assimilate radar/lightning-proxy reflectivity. First, cloud-to-ground lightning data were converted to reflectivity using a simple assumed relationship between flash density and reflectivity. Next, the reflectivity was used in the cloud analysis of GSI to adjust the cloud/hydrometeors and moisture. Additionally, the radar/lightning-proxy reflectivity was simultaneously converted to a 3D temperature tendency. Finally, the model-calculated temperature tendencies from the explicit microphysics scheme, as well as cumulus parameterization at 3D grid points at which the radar temperature tendency is available, were updated in a forward full-physics step of diabatic digital filter initialization in the WRF-ARW. The WRF-GSI system was tested using a mesoscale convective system that occurred on June 5, 2009, and by assimilating Doppler radar and lightning data, respectively. The forecasted reflectivity with assimilation corresponded more closely to the observed reflectivity than that of the parallel experiment without assimilation, particularly during the first 6 h. After assimilation, the short-range precipitation prediction improved, although the precipitation intensity was stronger than the observed one. In addition, the improvements obtained by assimilating lightning data were worse than those from assimilating radar reflectivity over the first 3 h but improved thereafter

Remote Sensing of Precipitation: Volume 2

Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne

A framework for the analysis of the influence of rainfall spatial organization and basin morphology on flood response

This work describes the derivation of a set of statistics, termed spatial moments of catchment rainfall, that quantify the dependence between rainfall spatial organization, basin morphology and runoff response. These statistics describe the spatial rainfall organisation in terms of concentration and dispersion along the flow distance coordinate. These statistics were derived starting from an analytical framework, and related with the statistical moments of the flood hydrograph. From spatial moments we also created an index quantifying catchment scale storm velocity. This index measures the overall movement of the rainfall system over the catchment, reflecting the filtering effect of its morphology. We also extended spatial moments to the hillslope system, developing a framework to evaluate the relevance of hillslope and channel propagation in the flood response to spatially variable rainfall fields. Data from six flash floods occurred in Europe between 2002 and 2007 are used to evaluate the information provided by the framework. High resolution radar rainfall fields and a distributed hydrologic model are employed to examine how effective are these statistics in describing the degree of spatial rainfall organisation, which is important for runoff modelling. The size of the study catchments ranges between 36 to 2586 km2. The analysis reported here shows that spatial moments of catchment rainfall can be effectively employed to isolate and describe the features of rainfall spatial organization which have significant impact on runoff simulation. Rainfall distribution was observed to play an important role in catchments as small as 50 km2. The description timing error was further improved by the inclusion in the framework of hillslope propagation. This development allows to compare scenarios of hillslope conditions, to evaluate the sensitivity of single basins or the effect of catchment scale. The analysis of catchment scale storm velocity showed a nonlinear dependence with basin scale. The values of velocity observed were however rather moderate, in spite of the strong kinematic characteristics of individual storm elements, and did not play a relevant effect on the flood analyzed

A Citizen-Science Approach for Urban Flood Risk Analysis Using Data Science and Machine Learning

Street flooding is problematic in urban areas, where impervious surfaces, such as concrete, brick, and asphalt prevail, impeding the infiltration of water into the ground. During rain events, water ponds and rise to levels that cause considerable economic damage and physical harm. The main goal of this dissertation is to develop novel approaches toward the comprehension of urban flood risk using data science techniques on crowd-sourced data. This is accomplished by developing a series of data-driven models to identify flood factors of significance and localized areas of flood vulnerability in New York City (NYC). First, the infrastructural (catch basin clogs, manhole issues, and sewer back-ups) and climatic (precipitation) contributions toward street flooding are investigated by using Stage IV radar precipitation data and crowd-sourced sewer reports (NYC 311 complaints), spanning a 10-year period. By applying a Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, with an embedded Zero-Inflation (ZI) model, the variables statistically significant as predictors, specific to each zip code, are detected. Second, with an intent to understand how factors affect the spatial variability of street flooding, the Random Forest regression machine learning algorithm is employed, where the 311 street flooding reports serve as the response, while the explanatory variables include topographic and land feature, physical and population dynamics, locational, infrastructural, and climatic influences. This model also analyzes socio-economic variables as predictors, as to allow for better insight into potential reporting biases within the NYC 311 crowdsourced platform. Third, utilizing the machine learning method of hierarchical clustering, the NYC zip codes are further analyzed for flood susceptibilities. The three variables are street flooding reports, catch basin blockages reports and radar precipitation data. Aggregated to the zip code level, the severe days of precipitation and street flood occurrence, over a ten-year period, are examined. Then, by the application of the algorithm, the zip codes with similar joint behavior (rainfall, street flooding and catch basin complaints) are clustered. Therefore, using crowdsourced data, three data driven models have been created, revealing the significant flood factors of NYC, the causes of variability among neighborhoods, and areas prone to urban flooding. Localized urban flood forecasting proves to be a difficult undertaking in major U.S. metropolitan areas. In these cities, the drainage information may be incomplete, or the access to the underground system may be restricted. Subsequently, with the capacity of the urban system unknown, traditional rainfall-runoff calculations are unrealistic. This research advances our knowledge of the variables associated with urban flooding, and, by various data analytic techniques, determine the extent of their effects within the study area of NYC. The research further builds upon this understanding of the factors to develop an urban risk zones map, pinpointing the localized areas (zip codes) of which street flooding will likely occur when there is a forecasted rain event. Utilizing regression and machine learning methodologies, with a unique investigation into infrastructural elements from crowd-sourced data, invaluable information towards advancements in urban flooding detection and prevention is provided