Concept of dealing with uncertainty in radar-based data for hydrological purpose

International audiencePrecipitation radar-based data constitute essential input to Numerical Weather Prediction (NWP) and rainfall-runoff models, however the data introduce a number of errors. Thus their uncertainty should be determined to provide end-users with more reliable information about forecasts. The common idea is to use Quality Index (QI) scheme for some number of quality parameters on the assumption that: (1) relationship between the parameters and relevant quality indexes is linear; (2) averaged QI is a weighted average of all particular indexes. The uncertainty parameters can be topography-dependent, resulting from spatial and temporal distribution of data, etc. Uncertainty in radar-based data is described by gamma PDF of precipitation, and it is proposed to determine the probability density function (PDF) parameters basing on QI values. Practically, precipitation is presented as ensemble of quantiles of the PDF and such an ensemble can constitute input to rainfall-runoff modelling. Since the ensemble is a precipitation input, the hydrological model needs to be activated according to a number of input members

Long-term multi-source precipitation estimation with high resolution (RainGRS Clim)

This paper explores the possibility of using multi-source precipitation estimates for climatological applications. A data-processing algorithm (RainGRS Clim) has been developed to work on precipitation accumulations such as daily or monthly totals, which are significantly longer than operational accumulations (generally between 5 min and 1 h). The algorithm makes the most of additional opportunities, such as the possibility of complementing data with delayed data, access to high-quality data that are not operationally available, and the greater efficiency of the algorithms for data quality control and merging with longer accumulations. Verification of the developed algorithms was carried out using monthly accumulations through comparison with precipitation from manual rain gauges. As a result, monthly accumulations estimated by RainGRS Clim were found to be significantly more reliable than accumulations generated operationally. This improvement is particularly noticeable for the winter months, when precipitation estimation is much more difficult due to less reliable radar estimates.</p

Precipitation Type Specific Radar Reflectivity-Rain Rate Relationships for Warsaw, Poland

Penelitian ini bertujuan untuk mengetahui peningkatan penguasaan konsep dan kemampuan literasi sains siswa dengan menggunakan model pembelajaran kontekstual berbantuan multimedia. Metode dan desain penelitian yang digunakan adalah quasi experiment dengan pretest-posttest control group design. Subjek penelitiannya adalah kelas XI di kabupaten Subang, Jawa-Barat. Hasil penelitian menunjukkan Model Pembelajaran Kontekstual berbantuan multimedia secara signifikan mampu meningkatkan penguasaan konsep dan kemampuan literasi sains siswa. Peningkatan penguasaan konsep siswa dengan nilai N-Gain 0.50 (kategori sedang) untuk kelas eksperimen dan 0,30 (kategori sedang) untuk kelas kontrol. Peningkatan kemampuan literasi sains siswa dengan nilai N-Gain 0.45 (kategori sedang) untuk kelas eksperimen dan 0,30 (kategori sedang) untuk kelas kontrol. This study aims to determine the concepts mastery and skills increase scientific literacy of students by using multimedia-assisted contextual learning model. The method used quasi experiment with pretest-posttest control group design. Subjects of study are class XI in Subang districts, West-Java. The result of study showed that contextual model’s aided by multimedia significantly enhance student’s concepts mastery and skills scientific literacy. The enhancement of student’s concepts mastery with N-Gain value is 0.50 (medium category) for experiment class and 0,30 (medium category) for control class. The enhancement of student's skills scientific literacy with N-Gain value is 0.45 (medium category) for experiment class and 0,30 (medium category) for control class

Quality-based generation of weather radar Cartesian products

Weather radar data volumes are commonly processed to obtain various 2-D Cartesian products based on the transfer from polar to Cartesian representations through a certain interpolation method. In this research an algorithm of the spatial interpolation of polar reflectivity data employing quality index data is applied to find the Cartesian reflectivity as plan position indicator products. On this basis, quality-based versions of standard algorithms for the generation of the following products have been developed: ETOP (echo top), MAX (maximum of reflectivity), and VIL (vertically integrated liquid water). Moreover, as an example of a higher-level product, a CONVECTION (detection of convection) has been defined as a specific combination of the above-listed standard products. A corresponding quality field is determined for each generated product, taking into account the quality of the pixels from which a given product was determined and how large a fraction of the investigated heights was scanned. Examples of such quality-based products are presented in the paper

Experiments with three-dimensional radar reflectivity data assimilation into the COAMPS model

High temporal and spatial resolution of radar measurements enables to continuously observe dynamically evolving meteorological phenomena. Three-dimensional (3D) weather radar reflectivity data assimilated into the numerical weather prediction model has the potential to improve initial description of the atmospheric model state. The paper is concentrated on the development of radar reflectivity assimilation technique into COAMPS mesoscale model using an Ensemble Kalman Filter (EnKF) type assimilation schemes available in Data Assimilation Research Testbed (DART) programming environment. Before weather radar data enter into the assimilation system, the measurement errors are eliminated through quality control procedures. At first artifacts associated with non-meteorological errors are removed using the algorithms based on analysis of reflectivity field pattern. Then procedures for correction of the reflectivity data are employed, especially due to radar beam blockage and attenuation in rain. Each of the correction algorithms is connected with generation of the data quality characteristic expressed quantitatively by so called quality index (QI). In order to avoid transformation of data uncertainty into assimilation scheme only the radar gates successfully verified by means of the quality algorithms were employed in the assimilation. The proposed methodology has been applied to simulate selected intense precipitation events in Poland in May and August 2010

Precipitation estimation and nowcasting at IMGW-PIB (SEiNO system)

A System for the Estimation and Nowcasting of Precipitation (SEiNO) is being developed at the Institute of Meteorology and Water Management – National Research Institute. Its aim is to provide the national meteorological and hydrological service with comprehensive operational tools for real-time high-resolution analyses and forecasts of precipitation fields. The system consists of numerical models for: (i) precipitation field analysis (estimation), (ii) precipitation nowcasting, i.e., extrapolation forecasting for short lead times, (iii) generation of probabilistic nowcasts. The precipitation estimation is performed by the conditional merging of information from telemetric rain gauges, the weather radar network, and the Meteosat satellite, employing quantitative quality information (quality index). Nowcasts are generated by three numerical models, employing various approaches to take account of different aspects of convective phenomena. Probabilistic forecasts are computed based on the investigation of deterministic forecast reliability determined in real time. Some elements of the SEiNO system are still under development and the system will be modernized continuously to reflect the progress in measurement techniques and advanced methods of meteorological data processing

MeteoGIS: GIS-based system for monitoring of severe meteorological phenomena

The MeteoGIS system developed at the Institute of Meteorology and Water Management – National Research Institute in Poland is a GIS-based system for real-time monitoring of weather and the generation of meteorological warnings. Apart from its monitoring features, it can also provide more advanced analysis, including SQL (Structured Query Language) queries and statistical analyses. Input data are provided mainly by the INCA-PL 2 nowcasting model which employs forecasts from the high-resolution AROME numerical weather prediction model and measurement data from the Polish weather radar network POLRAD and surface meteorological stations. As well as this, data from the PERUN lighting detection system are used. Ingestion of such data allows for the mitigation of risk from potentially hazardous weather phenomena such as extreme temperatures, strong wind, thunderstorms, heavy rain and subsequent impending floods. The following meteorological parameters at ground level are visualised in the MeteoGIS: (i) precipitation (accumulation and type), (ii) temperature, (iii) wind (speed and direction), (iv) lightning (locations and type). End users of the system are workers from civil protection services who are interested in shortterm warnings against severe weather events, especially area-oriented ones (related to districts, catchments, etc.). The reliability of visualised data is a very important issue, and from the MeteoGIS user’s point of view the improvement in data quality is a continuous process