scholarly journals Evaluation of high-resolution precipitation analyses using a dense station network

2015 ◽  
Vol 19 (3) ◽  
pp. 1547-1559 ◽  
Author(s):  
A. Kann ◽  
I. Meirold-Mautner ◽  
F. Schmid ◽  
G. Kirchengast ◽  
J. Fuchsberger ◽  
...  
Keyword(s):  
High Resolution ◽  
Prediction Models ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Rain Gauge ◽  
Convective Precipitation ◽  
Data Sets ◽  
Station Network ◽  
Temporal And Spatial ◽  
Categorical Verification

Abstract. The ability of radar–rain gauge merging algorithms to precisely analyse convective precipitation patterns is of high interest for many applications, e.g. hydrological modelling, thunderstorm warnings, and, as a reference, to spatially validate numerical weather prediction models. However, due to drawbacks of methods like cross-validation and due to the limited availability of reference data sets on high temporal and spatial scales, an adequate validation is usually hardly possible, especially on an operational basis. The present study evaluates the skill of very high-resolution and frequently updated precipitation analyses (rapid-INCA) by means of a very dense weather station network (WegenerNet), operated in a limited domain of the southeastern parts of Austria (Styria). Based on case studies and a longer-term validation over the convective season 2011, a general underestimation of the rapid-INCA precipitation amounts is shown by both continuous and categorical verification measures, although the temporal and spatial variability of the errors is – by convective nature – high. The contribution of the rain gauge measurements to the analysis skill is crucial. However, the capability of the analyses to precisely assess the convective precipitation distribution predominantly depends on the representativeness of the stations under the prevalent convective condition.

scholarly journals On the skill of high frequency precipitation analyses

2014 ◽  
Vol 11 (10) ◽  
pp. 11605-11636
Author(s):  
A. Kann ◽  
I. Meirold-Mautner ◽  
F. Schmid ◽  
G. Kirchengast ◽  
J. Fuchsberger
Keyword(s):  
High Frequency ◽  
Cross Validation ◽  
Rain Gauge ◽  
Convective Precipitation ◽  
Convective Condition ◽  
Station Network ◽  
Temporal And Spatial Variability ◽  
Temporal And Spatial ◽  
Merging Algorithms ◽  
Very High

Abstract. The ability of radar-rain gauge merging algorithms to precisely analyse convective precipitation patterns is of high interest for many applications, e.g. hydrological modelling. However, due to drawbacks of methods like cross-validation and due to the limited availability of reference datasets on high temporal and spatial scale, an adequate validation is usually hardly possible, especially on an operational basis. The present study evaluates the skill of very high resolution and frequently updated precipitation analyses (rapid-INCA) by means of a very dense station network (WegenerNet), operated in a limited domain of the south-eastern parts of Austria (Styria). Based on case studies and a longer term validation over the convective season 2011, a general underestimation of the rapid-INCA precipitation amounts is shown, although the temporal and spatial variability of the errors is – by convective nature – high. The contribution of the rain gauge measurements to the analysis skill is crucial. However, the capability of the analyses to precisely assess the convective precipitation distribution predominantly depends on the representativeness of the stations under the prevalent convective condition.

A downscaling framework for precipitation nowcasting by merging radar retrievals at different scales and resolutions

2021 ◽  
Author(s):  
Roberto Deidda ◽  
Stefano Farris ◽  
Maria Grazia Badas ◽  
Marino Marrocu ◽  
Luca Massidda ◽  
...  
Keyword(s):  
High Resolution ◽  
Prediction Models ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Weather Radar ◽  
Early Warning Systems ◽  
Small Scale ◽  
Time Step ◽  
Radar Network ◽  
X Band

<p>Convective rainfall events represent one of the most critical issues in urban areas, where numerical weather prediction models are affected by a large uncertainty related to the short temporal and spatial scales involved, thus making early warning systems ineffective. Conversely, radar-based nowcasting models may be a useful tool to guarantee short-term forecasts, through the extrapolation of most recent properties in observed precipitation fields, for lead times ranging from minutes to few hours.</p><p>In this study we develop a procedure for merging relevant information from two radar products with different resolutions and scales: (i) high-resolution observations retrieved by an X-band weather radar in a small domain (the metropolitan area of Cagliari, located in Sardinia, Italy), and (ii) the mosaic data provided by the Italian Civil Protection national radar network (the whole region of Sardinia). Specifically, we here adapt some STEPS procedures to merge the large-scale advection from the latter radar network, and the small-scale statistical properties for the former X-band weather radar. We thus combine the corresponding forecasts preserving the higher resolution scale. In details, for each time step we (i) evaluate the power spectra of the two forecasts (ii) merge the two spectra taking the power of the large (small) frequencies from the high (low) resolution data spectrum and (iii) achieve optimal downscaling by reconstructing the high-resolution nowcast from the blend of the two spectra.</p>

scholarly journals Improving Numerical Weather Predictions of Summertime Precipitation over the Southeastern United States through a High-Resolution Initialization of the Surface State

2011 ◽  
Vol 26 (6) ◽  
pp. 785-807 ◽  
Author(s):  
Jonathan L. Case ◽  
Sujay V. Kumar ◽  
Jayanthi Srikishen ◽  
Gary J. Jedlovec
Keyword(s):  
United States ◽  
Soil Moisture ◽  
High Resolution ◽  
Land Surface ◽  
Weather Prediction ◽  
Southeastern United States ◽  
Model Verification ◽  
Sea Surface ◽  
Convective Precipitation ◽  
Numerical Weather

Abstract It is hypothesized that high-resolution, accurate representations of surface properties such as soil moisture and sea surface temperature are necessary to improve simulations of summertime pulse-type convective precipitation in high-resolution models. This paper presents model verification results of a case study period from June to August 2008 over the southeastern United States using the Weather Research and Forecasting numerical weather prediction model. Experimental simulations initialized with high-resolution land surface fields from the National Aeronautics and Space Administration’s (NASA) Land Information System (LIS) and sea surface temperatures (SSTs) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) are compared to a set of control simulations initialized with interpolated fields from the National Centers for Environmental Prediction’s (NCEP) 12-km North American Mesoscale model. The LIS land surface and MODIS SSTs provide a more detailed surface initialization at a resolution comparable to the 4-km model grid spacing. Soil moisture from the LIS spinup run is shown to respond better to the extreme rainfall of Tropical Storm Fay in August 2008 over the Florida peninsula. The LIS has slightly lower errors and higher anomaly correlations in the top soil layer but exhibits a stronger dry bias in the root zone. The model sensitivity to the alternative surface initial conditions is examined for a sample case, showing that the LIS–MODIS data substantially impact surface and boundary layer properties. The Developmental Testbed Center’s Meteorological Evaluation Tools package is employed to produce verification statistics, including traditional gridded precipitation verification and output statistics from the Method for Object-Based Diagnostic Evaluation (MODE) tool. The LIS–MODIS initialization is found to produce small improvements in the skill scores of 1-h accumulated precipitation during the forecast hours of the peak diurnal convective cycle. Because there is very little union in time and space between the forecast and observed precipitation systems, results from the MODE object verification are examined to relax the stringency of traditional gridpoint precipitation verification. The MODE results indicate that the LIS–MODIS-initialized model runs increase the 10 mm h−1 matched object areas (“hits”) while simultaneously decreasing the unmatched object areas (“misses” plus “false alarms”) during most of the peak convective forecast hours, with statistically significant improvements of up to 5%. Simulated 1-h precipitation objects in the LIS–MODIS runs more closely resemble the observed objects, particularly at higher accumulation thresholds. Despite the small improvements, however, the overall low verification scores indicate that much uncertainty still exists in simulating the processes responsible for airmass-type convective precipitation systems in convection-allowing models.

scholarly journals Hydro-meteorological reconstruction and geomorphological impact assessment of the October 2018 catastrophic flash flood at Sant Llorenç, Mallorca (Spain)

2019 ◽  
Vol 19 (11) ◽  
pp. 2597-2617 ◽  
Author(s):  
Jorge Lorenzo-Lacruz ◽  
Arnau Amengual ◽  
Celso Garcia ◽  
Enrique Morán-Tejeda ◽  
Víctor Homar ◽  
...  
Keyword(s):  
Prediction Models ◽  
Extreme Event ◽  
Response Times ◽  
Flash Flood ◽  
Weather Prediction ◽  
Early Warning Systems ◽  
Rain Gauge ◽  
Management Service ◽  
Convective Rainfall ◽  
Hydraulic Simulation

Abstract. An extraordinary convective rainfall event, unforeseen by most numerical weather prediction models, generated a devastating flash flood (305 m3 s−1) in the town of Sant Llorenç des Cardassar, Mallorca, on 9 October 2018. Four people died inside this village, while casualties were up to 13 over the entire affected area. This extreme event has been reconstructed by implementing an integrated flash flood modelling approach in the Ses Planes catchment up to Sant Llorenç (23.4 km2), based on three components: (i) generation of radar-derived precipitation estimates, (ii) modelling of accurate discharge hydrographs yielded by the catchment (using FEST and KLEM models), and (iii) hydraulic simulation of the event and mapping of affected areas (using HEC-RAS). Radar-derived rainfall estimates show very high agreement with rain gauge data (R2=0.98). Modelled flooding extent is in close agreement with the observed extension by the Copernicus Emergency Management Service, based on Sentinel-1 imagery, and both far exceed the extension for a 500-year return period flood. Hydraulic simulation revealed that water reached a depth of 3 m at some points, and modelled water depths highly correlate (R2=0.91) with in situ after-event measurements. The 9 October flash flood eroded and transported woody and abundant sediment debris, changing channel geomorphology. Water velocity greatly increased at bridge locations crossing the river channel, especially at those closer to the Sant Llorenç town centre. This study highlights how the very low predictability of this type of extreme convective rainfall events and the very short hydrological response times typical of small Mediterranean catchments continue to challenge the implementation of early warning systems, which effectively reduce people's exposure to flash flood risk in the region.

scholarly journals Forecasting of flash floods by Algorithm of Storm Prediction

2018 ◽  
Vol 210 ◽  
pp. 04033 ◽  
Author(s):  
David Šaur ◽  
Kateřina Víchová
Keyword(s):  
Prediction Models ◽  
Preventive Measures ◽  
Weather Prediction ◽  
Flash Floods ◽  
Convective Precipitation ◽  
Result Section ◽  
Rescue Service ◽  
Weather Events ◽  
Storm Prediction ◽  
Numerical Weather Prediction Models

This article focuses on the forecasting of flash floods using the Algorithm of Storm Prediction as a new tool to predict convective precipitation, severe phenomena and the risk of flash floods. The first part of the article contains information on methods for predicting dangerous severe phenomena. This algorithm uses mainly data from numerical weather prediction models (NWP models), database of historic weather events and relief characteristics describing the influence of orography on the initiation of atmospheric convection. The result section includes verification of predicted algorithm outputs, selected NWP models and warnings of CHMI and ESTOFEX on three events related to the floods that hit the Zlín Region between years of 2015 - 2017. The main result is a report with prediction outputs of the algorithm visualized in maps for the territory of municipalities with extended competence and their regions. The outputs of the algorithm will be used primarily to increase the effectiveness of preventive measures against flash floods not only by the Fire Rescue Service of Czech Republic but also by the flood and crisis management authorities.

scholarly journals Validation of the First Years of GPM Operation over Cyprus

2018 ◽  
Vol 10 (10) ◽  
pp. 1520 ◽  
Author(s):  
Adrianos Retalis ◽  
Dimitris Katsanos ◽  
Filippos Tymvios ◽  
Silas Michaelides
Keyword(s):  
High Resolution ◽  
Prediction Models ◽  
Weather Prediction ◽  
Monthly Basis ◽  
Rain Gauges ◽  
Precipitation Measurement ◽  
Precipitation Estimates ◽  
First Results ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Global Precipitation Measurement (GPM) high-resolution product is validated against rain gauges over the island of Cyprus for a three-year period, starting from April 2014. The precipitation estimates are available in both high temporal (half hourly) and spatial (10 km) resolution and combine data from all passive microwave instruments in the GPM constellation. The comparison performed is twofold: first the GPM data are compared with the precipitation measurements on a monthly basis and then the comparison focuses on extreme events, recorded throughout the first 3 years of GPM’s operation. The validation is based on ground data from a dense and reliable network of rain gauges, also available in high temporal (hourly) resolution. The first results show very good correlation regarding monthly values; however, the correspondence of GPM in extreme precipitation varies from “no correlation” to “high correlation”, depending on case. This study aims to verify the GPM rain estimates, since such a high-resolution dataset has numerous applications, including the assimilation in numerical weather prediction models and the study of flash floods with hydrological models.

scholarly journals Variance-Based Sensitivity Analysis: Preliminary Results in COAMPS

2014 ◽  
Vol 142 (5) ◽  
pp. 2028-2042 ◽  
Author(s):  
Caren Marzban ◽  
Scott Sandgathe ◽  
James D. Doyle ◽  
Nicholas C. Lederer
Keyword(s):  
Sensitivity Analysis ◽  
Prediction Models ◽  
Theoretical Calculations ◽  
Weather Prediction ◽  
Surface Fluxes ◽  
Cumulus Parameterization ◽  
Convective Precipitation ◽  
Primary Concern ◽  
Total Precipitation ◽  
Parameter Values

Abstract Numerical weather prediction models have a number of parameters whose values are either estimated from empirical data or theoretical calculations. These values are usually then optimized according to some criterion (e.g., minimizing a cost function) in order to obtain superior prediction. To that end, it is useful to know which parameters have an effect on a given forecast quantity, and which do not. Here the authors demonstrate a variance-based sensitivity analysis involving 11 parameters in the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS). Several forecast quantities are examined: 24-h accumulated 1) convective precipitation, 2) stable precipitation, 3) total precipitation, and 4) snow. The analysis is based on 36 days of 24-h forecasts between 1 January and 4 July 2009. Regarding convective precipitation, not surprisingly, the most influential parameter is found to be the fraction of available precipitation in the Kain–Fritsch cumulus parameterization fed back to the grid scale. Stable and total precipitation are most affected by a linear factor that multiplies the surface fluxes; and the parameter that most affects accumulated snow is the microphysics slope intercept parameter for snow. Furthermore, all of the interactions between the parameters are found to be either exceedingly small or have too much variability (across days and/or parameter values) to be of primary concern.

scholarly journals Long-range transport of terrain-induced turbulence from high-resolution numerical simulations

2011 ◽  
Vol 11 (22) ◽  
pp. 11793-11805 ◽  
Author(s):  
M. Katurji ◽  
S. Zhong ◽  
P. Zawar-Reza
Keyword(s):  
High Resolution ◽  
Aspect Ratio ◽  
Numerical Simulations ◽  
Long Range ◽  
Prediction Models ◽  
Atmospheric Transport ◽  
Weather Prediction ◽  
Measurement Technology ◽  
Topographic Features ◽  
Range Transport

Abstract. Over complex terrain, an important question is how various topographic features may generate or alter wind turbulence and how far the influence can be extended downstream. Current measurement technology limits the capability in providing a long-range snapshot of turbulence as atmospheric eddies travel over terrain, interact with each other, change their productive and dissipative properties, and are then observed tens of kilometers downstream of their source. In this study, we investigate through high-resolution numerical simulations the atmospheric transport of terrain-generated turbulence in an atmosphere that is neutrally stratified. The simulations are two-dimensional with an isotropic spatial resolution of 15 m and run to a quasi-steady state. They are designed in such a way to allow an examination of the effects of a bell-shaped experimental hill with varying height and aspect ratio on turbulence properties generated by another hill 20 km upstream. Averaged fields of the turbulent kinetic energy (TKE) imply that terrain could have a large influence on velocity perturbations at least 30H (H is the terrain height) upstream and downstream of the terrain, with the largest effect happening in the area of the largest pressure perturbations. The results also show that downstream of the terrain the TKE fields are sensitive to the terrain's aspect ratio with larger enhancement in turbulence by higher aspect ratio, while upstream there is a suppression of turbulence that does not appear to be sensitive to the terrain aspect ratio. Instantaneous vorticity fields shows very detailed flow structures that resemble a multitude of eddy scales dynamically interacting while shearing oppositely paired vortices. The knowledge of the turbulence production and modifications by topography from these high-resolution simulations can be helpful in understanding long-range terrain-induced turbulence and improving turbulence parameterizations used in lower resolution weather prediction models.

scholarly journals An Early Performance Evaluation of the NEXRAD Dual-Polarization Radar Rainfall Estimates for Urban Flood Applications

2013 ◽  
Vol 28 (6) ◽  
pp. 1478-1497 ◽  
Author(s):  
Luciana K. Cunha ◽  
James A. Smith ◽  
Mary Lynn Baeck ◽  
Witold F. Krajewski
Keyword(s):  
Kansas City ◽  
Spatial Scales ◽  
Rain Gauge ◽  
Dual Polarization ◽  
Radar Rainfall ◽  
Urban Flood ◽  
Temporal And Spatial ◽  
Single Polarization ◽  
Early Performance ◽  
Rainfall Estimates

Abstract Dual-polarization radars are expected to provide better rainfall estimates than single-polarization radars because of their ability to characterize hydrometeor type. The goal of this study is to evaluate single- and dual-polarization radar rainfall fields based on two overlapping radars (Kansas City, Missouri, and Topeka, Kansas) and a dense rain gauge network in Kansas City. The study area is located at different distances from the two radars (23–72 km for Kansas City and 104–157 km for Topeka), allowing for the investigation of radar range effects. The temporal and spatial scales of radar rainfall uncertainty based on three significant rainfall events are also examined. It is concluded that the improvements in rainfall estimation achieved by polarimetric radars are not consistent for all events or radars. The nature of the improvement depends fundamentally on range-dependent sampling of the vertical structure of the storms and hydrometeor types. While polarimetric algorithms reduce range effects, they are not able to completely resolve issues associated with range-dependent sampling. Radar rainfall error is demonstrated to decrease as temporal and spatial scales increase. However, errors in the estimation of total storm accumulations based on polarimetric radars remain significant (up to 25%) for scales of approximately 650 km2.

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