Weather Radar Precipitation Data And Their Use In Hydrological Modelling

1990 ◽  
pp. 143-163 ◽  
Author(s):  
C. G. Collier
Keyword(s):  
Weather Radar ◽  
Hydrological Modelling ◽  
Precipitation Data

scholarly journals Facilitating radar precipitation data processing, assessment and analysis: a GIS-compatible python approach

2019 ◽  
Vol 21 (4) ◽  
pp. 652-670 ◽  
Author(s):  
Jennifer Kreklow
Keyword(s):  
Data Processing ◽  
Data Exchange ◽  
Weather Radar ◽  
Radar Data ◽  
Rain Gauge ◽  
Precipitation Data ◽  
Raw Data ◽  
Automated Processing ◽  
Point Data ◽  
Radar Data Processing

Abstract A review of existing tools for radar data processing revealed a lack of open source software for automated processing, assessment and analysis of weather radar composites. The ArcGIS-compatible Python package radproc attempts to reduce this gap. Radproc provides an automated raw data processing workflow for nationwide, freely available German weather radar climatology (RADKLIM) and operational (RADOLAN) composite products. Raw data are converted into a uniform HDF5 file structure used by radproc's analysis and data quality assessment functions. This enables transferability of the developed analysis and export functionality to other gridded or point-scale precipitation data. Thus, radproc can be extended by additional import routines to support any other German or non-German precipitation dataset. Analysis methods include temporal aggregations, detection of heavy rainfall and an automated processing of rain gauge point data into the same HDF5 format for comparison to gridded radar data. A set of functions for data exchange with ArcGIS allows for visualisation and further geospatial analysis. The application on a 17-year time series of hourly RADKLIM data showed that radproc greatly facilitates radar data processing and analysis by avoiding manual programming work and helps to lower the barrier for non-specialists to work with these novel radar climatology datasets.

scholarly journals A GIS methodology for the analysis of weather radar precipitation data

2003 ◽  
Vol 5 (2) ◽  
pp. 113-126 ◽  
Author(s):  
M. A. Gad ◽  
I. K. Tsanis
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
Rain Gauge ◽  
Precipitation Data ◽  
Rain Gauge Data ◽  
Gis Environment ◽  
Arcview Gis ◽  
The City ◽  
Rain Gauge Network

A GIS multi-component module was developed within the ArcView GIS environment for processing and analysing weather radar precipitation data. The module is capable of: (a) reading geo-reference radar data and comparing it with rain-gauge network data, (b) estimating the kinematics of rainfall patterns, such as the storm speed and direction, and (c) accumulating radar-derived rainfall depths. By bringing the spatial capabilities of GIS to bear this module can accurately locate rainfall on the ground and can overlay the animated storm on different geographical features of the study area, making the exploration of the storm's kinematic characteristics obtained from radar data relatively simple. A case study in the City of Hamilton in Ontario, Canada is used to demonstrate the functionality of the module. Radar comparison with rain gauge data revealed an underestimation of the classical Marshal & Palmer Z–R relation to rainfall rate.

scholarly journals Evaluation of areal precipitation estimates based on downscaled reanalysis and station data by hydrological modelling

2013 ◽  
Vol 17 (7) ◽  
pp. 2415-2434 ◽  
Author(s):  
D. Duethmann ◽  
J. Zimmer ◽  
A. Gafurov ◽  
A. Güntner ◽  
D. Kriegel ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Reanalysis Data ◽  
Hydrological Modelling ◽  
Performance Criteria ◽  
Data Sets ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Mountainous Catchments ◽  
Precipitation Estimates ◽  
Areal Precipitation

Abstract. In data sparse mountainous regions it is difficult to derive areal precipitation estimates. In addition, their evaluation by cross validation can be misleading if the precipitation gauges are not in representative locations in the catchment. This study aims at the evaluation of precipitation estimates in data sparse mountainous catchments. In particular, it is first tested whether monthly precipitation fields from downscaled reanalysis data can be used for interpolating gauge observations. Secondly, precipitation estimates from this and other methods are evaluated by comparing simulated and observed discharge, which has the advantage that the data are evaluated at the catchment scale. This approach is extended here in order to differentiate between errors in the overall bias and the temporal dynamics, and by taking into account different sources of uncertainties. The study area includes six headwater catchments of the Karadarya Basin in Central Asia. Generally the precipitation estimate based on monthly precipitation fields from downscaled reanalysis data showed an acceptable performance, comparable to another interpolation method using monthly precipitation fields from multi-linear regression against topographical variables. Poor performance was observed in only one catchment, probably due to mountain ridges not resolved in the model orography of the regional climate model. Using two performance criteria for the evaluation by hydrological modelling allowed a more informed differentiation between the precipitation data and showed that the precipitation data sets mostly differed in their overall bias, while the performance with respect to the temporal dynamics was similar. Our precipitation estimates in these catchments are considerably higher than those from continental- or global-scale gridded data sets. The study demonstrates large uncertainties in areal precipitation estimates in these data sparse mountainous catchments. In such regions with only very few precipitation gauges but high spatial variability of precipitation, important information for evaluating precipitation estimates may be gained by hydrological modelling and a comparison to observed discharge.

Weather Radar Data for Hydrological Modelling: An Application for South of Primorye Region, Russia

2019 ◽  
Vol 46 (S2) ◽  
pp. S25-S30 ◽  
Author(s):  
L. V. Gonchukov ◽  
A. N. Bugaets ◽  
B. I. Gartsman ◽  
K. T. Lee
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
Hydrological Modelling ◽  
Primorye Region

scholarly journals Weather Radar Data and Distributed Hydrological Modelling: An Application for Mexico Valley

2013 ◽  
Vol 03 (02) ◽  
pp. 79-88 ◽  
Author(s):  
Baldemar Méndez-Antonio ◽  
Ernesto Caetano ◽  
Gabriel Soto-Cortés ◽  
Fabián G. Rivera-Trejo ◽  
Ricardo A. Carvajal Rodríguez ◽  
...  
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
Hydrological Modelling ◽  
Distributed Hydrological Modelling

scholarly journals Evaluation of areal precipitation estimates based on downscaled reanalysis and station data by hydrological modelling

2012 ◽  
Vol 9 (9) ◽  
pp. 10719-10773 ◽  
Author(s):  
D. Duethmann ◽  
J. Zimmer ◽  
A. Gafurov ◽  
A. Güntner ◽  
B. Merz ◽  
...  
Keyword(s):  
Reanalysis Data ◽  
Hydrological Modelling ◽  
Mean Annual Precipitation ◽  
Model Parameters ◽  
Data Sets ◽  
Precipitation Data ◽  
Data Set ◽  
Station Data ◽  
Precipitation Estimates ◽  
Areal Precipitation

Abstract. In data sparse regions, as in many mountainous catchments, it is a challenge to generate suitable precipitation input fields for hydrological modelling, as station data do not provide enough information to derive areal precipitation estimates. This study presents a method using the spatial variation of precipitation from downscaled reanalysis data for the interpolation of gauge observations. The second aim of this study is the evaluation of different precipitation estimates by hydrological modelling. Study area is the Karadarya catchment in Central Asia (11 700 km2). ERA-40 reanalysis data are downscaled with the regional climate model Weather Research and Forecasting Model (WRF). Precipitation data from gauge observations are interpolated (i) using monthly accumulated WRF precipitation data, (ii) using monthly fields from multiple linear regression against topographical variables and (iii) with the inverse distance approach. These precipitation data sets are also compared to (iv) the direct use of the precipitation output from the WRF downscaled ERA-40 data and (v) precipitation from the APHRODITE data set. Our study suggests that using monthly fields from downscaled reanalysis data can be a good approach for the interpolation of station data in data sparse mountainous regions. Compared to mean annual precipitation from continental and global scale gridded data sets our precipitation estimates for the study area are considerably higher. The introduction of a calibrated precipitation bias factor for the comparison of different precipitation estimates by hydrological modelling allows for a more informed differentiation with regard to the temporal dynamics, on the one hand, and the overall bias, on the other hand. Uncertainty and sensitivity analyses suggest that our results are robust against uncertainties in the calibration parameters, other model parameters and inputs, and the selected calibration period.

Polarimetric Doppler Weather Radar

2001 ◽  
Author(s):  
V. N. Bringi ◽  
V. Chandrasekar
Keyword(s):  
Weather Radar ◽  
Doppler Weather Radar
Sign in / Sign up

Export Citation Format

Share Document