Abstract
A quantitative precipitation estimate (QPE) provides basic information for the modelling of many kinds of hydro-meteorological processes, e.g., as input to rainfall-runoff models for flash flood forecasting. Weather radar observations are crucial in order to meet the requirements, because of their very high temporal and spatial resolution. Other sources of precipitation data, such as telemetric rain gauges and satellite observations, are also included in the QPE. All of the used data are characterized by different temporal and spatial error structures. Therefore, a combination of the data should be based on quality information quantitatively determined for each input to take advantage of a particular source of precipitation measurement. The presented work on multi-source QPE, being implemented as the RainGRS system, has been carried out in the Polish national meteorological and hydrological service for new nowcasting and hydrological platforms in Poland. For each of the three data sources, different quality algorithms have been designed: (i) rain gauge data is quality controlled and, on this basis, spatial interpolation and estimation of quality field is performed, (ii) radar data are quality controlled by RADVOL-QC software that corrects errors identified in the data and characterizes its final quality, (iii) NWC SAF (Satellite Application Facility on support to Nowcasting and Very Short Range Forecasting) products for both visible and infrared channels are combined and the relevant quality field is determined from empirical relationships that are based on analyses of the product performance. Subsequently, the quality-based QPE is generated with a 1-km spatial resolution every 10 minutes (corresponding to radar data). The basis for the combination is a conditional merging technique that is enhanced by involving detailed quality information that is assigned to individual input data. The validation of the RainGRS estimates was performed taking account of season and kind of precipitation.
Highlights
The estimation of ground precipitation field with high spatial and temporal resolution is a crucial problem from the perspective of modern meteorology and hydrology
The spatially distributed quality index field QIG of the estimated precipitation field Gint is determined on the basis of data from point telemetric rain gauges, taking the two quality factors into consideration: (i) qualities of all gauges expressed by their QIs and (ii) density of the gauge network and completeness of data expressed by distance to the nearest gauge
The investigation focused on the development of a technique for the combination of data provided by a telemetric rain gauge network, weather radar network, and meteorological satellite in order to obtain a quantitative precipitation estimate, i.e., an optimal precipitation field as close to the ground truth as possible
Summary
The estimation of ground precipitation field with high spatial and temporal resolution is a crucial problem from the perspective of modern meteorology and hydrology. A starting point for the methodology employed in this work is a conditional combination technique proposed by Sinclair and Pegram [17] In this approach, rain gauges are considered to be the most precise technique for delivering information regarding the precipitation rate at a gauge location, whereas the others deliver unique information about spatial distribution of a precipitation field. Fields of quality index QI for particular kinds of precipitation data are determined by employing different algorithms because they are characterized by a very different error structure, so different quality factors are taken into consideration to determine the fields This approach was implemented as the RainGRS system, which has been working operationally from 2015 in the Polish national meteorological and hydrological service, i.e., the Institute of Meteorology. The results of the validation of the proposed techniques with examples are presented (Section 4) and the paper finishes with conclusions (Section 5)
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