scholarly journals Comparison of the GPM IMERG Final Precipitation Product to RADOLAN Weather Radar Data over the Topographically and Climatically Diverse Germany

2018 ◽  
Vol 10 (12) ◽  
pp. 2029 ◽  
Author(s):  
Thomas Ramsauer ◽  
Thomas Weiß ◽  
Philip Marzahn
Keyword(s):  
Spatial Variability ◽  
Winter Season ◽  
Weather Radar ◽  
Radar Data ◽  
Probability Of Detection ◽  
Statistical Measures ◽  
Weather Events ◽  
Precipitation Measurement ◽  
Spatio Temporal ◽  
Global Precipitation

Precipitation measurements provide crucial information for hydrometeorological applications. In regions where typical precipitation measurement gauges are sparse, gridded products aim to provide alternative data sources. This study examines the performance of NASA’s Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement Mission (IMERG, GPM) satellite precipitation dataset in capturing the spatio-temporal variability of weather events compared to the German weather radar dataset RADOLAN RW. Besides quantity, also timing of rainfall is of very high importance when modeling or monitoring the hydrologic cycle. Therefore, detection metrics are evaluated along with standard statistical measures to test both datasets. Using indices like “probability of detection” allows a binary evaluation showing the basic categorical accordance of the radar and satellite data. Furthermore, a pixel-by-pixel comparison is performed to assess the ability to represent the spatial variability of rainfall and precipitation quantity. All calculations are additionally carried out for seasonal subsets of the data to assess potentially different behavior due to differences in precipitation schemes. The results indicate significant differences between the datasets. Overall, GPM IMERG overestimates the quantity of precipitation compared to RADOLAN, especially in the winter season. Moreover, shortcomings in detection performance arise in this season with significant erroneously-detected, yet also missed precipitation events compared to the weather radar data. Additionally, along secondary mountain ranges and the Alps, topographically-induced precipitation is not represented in GPM data, which generally shows a lack of spatial variability in rainfall and snowfall estimates due to lower resolution.

scholarly journals Satellite-based high-resolution mapping of rainfall over southern Africa

2017 ◽  
Vol 10 (6) ◽  
pp. 2009-2019 ◽  
Author(s):  
Hanna Meyer ◽  
Johannes Drönner ◽  
Thomas Nauss
Keyword(s):  
Southern Africa ◽  
Ground Truth ◽  
Skill Score ◽  
Probability Of Detection ◽  
Rainfall Events ◽  
Precipitation Measurement ◽  
Hourly Rainfall ◽  
Model Probability ◽  
Spatio Temporal ◽  
Global Precipitation

Abstract. A spatially explicit mapping of rainfall is necessary for southern Africa for eco-climatological studies or nowcasting but accurate estimates are still a challenging task. This study presents a method to estimate hourly rainfall based on data from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI). Rainfall measurements from about 350 weather stations from 2010–2014 served as ground truth for calibration and validation. SEVIRI and weather station data were used to train neural networks that allowed the estimation of rainfall area and rainfall quantities over all times of the day. The results revealed that 60 % of recorded rainfall events were correctly classified by the model (probability of detection, POD). However, the false alarm ratio (FAR) was high (0.80), leading to a Heidke skill score (HSS) of 0.18. Estimated hourly rainfall quantities were estimated with an average hourly correlation of ρ = 0. 33 and a root mean square error (RMSE) of 0.72. The correlation increased with temporal aggregation to 0.52 (daily), 0.67 (weekly) and 0.71 (monthly). The main weakness was the overestimation of rainfall events. The model results were compared to the Integrated Multi-satellitE Retrievals for GPM (IMERG) of the Global Precipitation Measurement (GPM) mission. Despite being a comparably simple approach, the presented MSG-based rainfall retrieval outperformed GPM IMERG in terms of rainfall area detection: GPM IMERG had a considerably lower POD. The HSS was not significantly different compared to the MSG-based retrieval due to a lower FAR of GPM IMERG. There were no further significant differences between the MSG-based retrieval and GPM IMERG in terms of correlation with the observed rainfall quantities. The MSG-based retrieval, however, provides rainfall in a higher spatial resolution. Though estimating rainfall from satellite data remains challenging, especially at high temporal resolutions, this study showed promising results towards improved spatio-temporal estimates of rainfall over southern Africa.

scholarly journals Satellite based high resolution mapping of rainfall over Southern Africa

2017 ◽  
Author(s):  
Hanna Meyer ◽  
Johannes Drönner ◽  
Thomas Nauss
Keyword(s):  
Southern Africa ◽  
Skill Score ◽  
Probability Of Detection ◽  
High Temporal Resolution ◽  
Rainfall Events ◽  
Precipitation Measurement ◽  
Hourly Rainfall ◽  
Model Probability ◽  
Spatio Temporal ◽  
Global Precipitation

Abstract. A spatially explicit mapping of rainfall is highly required for Southern Africa for eco-climatological studies or nowcasting but accurate estimates are still a challenging task. This study presents a method to estimate hourly rainfall based on data from the Meteosat Second Generation (MSG) spinning enhanced visible and infrared imager (SEVIRI). Rainfall measurements from about 350 weather stations from the years 2010–2014 served as ground truths for calibration and validation. SEVIRI and weather station data were used to train neural networks that allowed predicting rainfall area and rainfall quantities during all times of the day. The results revealed that 60 % of recorded rainfall events were correctly classified by the model (Probability of detection, POD). However, the false alarm ratio (FAR) was high (0.80), leading to an Heidke Skill Score (HSS) of 0.18. Predicted hourly rainfall quantities were estimated with an average hourly correlation of rho = 0.33 and a RMSE of 0.72. The correlation increased with temporal aggregation to 0.52 (daily), 0.67 (weekly) and 0.71 (monthly). The main weakness was the overestimation of rainfall events. The model results were compared to the IMERG product of the Global Precipitation Measurement (GPM) mission. Despite being a comparably simple approach, the presented MSG based rainfall retrieval outperformed GPM IMERG in terms of rainfall area detection where GPM IMERG had a considerably lower POD. The HSS was not significantly different compared to the MSG based retrieval due to a lower FAR of GPM IMERG. There were no further significant differences between the MSG based retrieval and GPM IMERG in terms of correlation with the observed rainfall quantities. The MSG based retrieval, however, provides rainfall in higher spatial resolution. Though it remains challenging to estimate rainfall from satellite data, especially on a high temporal resolution, this study showed promising results towards improved spatio-temporal estimates of rainfall over Southern Africa.

scholarly journals Detecting and Tracking Communal Bird Roosts in Weather Radar Data

2020 ◽  
Vol 34 (01) ◽  
pp. 378-385
Author(s):  
Zezhou Cheng ◽  
Saadia Gabriel ◽  
Pankaj Bhambhani ◽  
Daniel Sheldon ◽  
Subhransu Maji ◽  
...  
Keyword(s):  
Latent Variable ◽  
Weather Radar ◽  
Radar Data ◽  
Learning System ◽  
Variable Model ◽  
Detection Model ◽  
Radar Images ◽  
Biological Phenomena ◽  
The Us ◽  
Spatio Temporal

The US weather radar archive holds detailed information about biological phenomena in the atmosphere over the last 20 years. Communally roosting birds congregate in large numbers at nighttime roosting locations, and their morning exodus from the roost is often visible as a distinctive pattern in radar images. This paper describes a machine learning system to detect and track roost signatures in weather radar data. A significant challenge is that labels were collected opportunistically from previous research studies and there are systematic differences in labeling style. We contribute a latent-variable model and EM algorithm to learn a detection model together with models of labeling styles for individual annotators. By properly accounting for these variations we learn a significantly more accurate detector. The resulting system detects previously unknown roosting locations and provides comprehensive spatio-temporal data about roosts across the US. This data will provide biologists important information about the poorly understood phenomena of broad-scale habitat use and movements of communally roosting birds during the non-breeding season.

Nowcasting lightning during RELAMPAGO

2020 ◽  
Author(s):  
Evan Ruzanski ◽  
Venkatachalam Chandrasekar ◽  
Ivan Arias
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
The United States ◽  
Extreme Weather Events ◽  
Optimization Approach ◽  
Lightning Flash ◽  
Convective Systems ◽  
Physical Mechanisms ◽  
Weather Events ◽  
Future Loss

<p>The Remote Sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observations (RELAMPAGO) international field campaign occurred June 1, 2018, to April 30, 2019. This campaign was comprised of more than 150 scientists from 10 organizations. Data was collected to investigate different phases of the life cycle of thunderstorms that occur in Argentina to better understand the physical mechanisms that cause the initiation and growth of organized convective systems in some of the most intense storms on the planet. The main focus of the project was to develop new conceptual models for forecasting extreme weather events that will hopefully lead to reductions in future loss of life and property.</p><p>This presentation shows the performance of a recently developed model for estimating ice mass aloft, a key component in the atmospheric electrification process, and a method for nowcasting lightning activity using C-band weather radar and Global Lightning Dataset (GLD360) data from RELAMPAGO. This nowcasting method uses a grid-based approach to make specific forecasts of lightning in space and time. The method estimates ice mass aloft in the region where electrification occurs using a numerical optimization approach to essentially reframe a simplified bulk microphysical model into a completely data-driven model. Previous results using WSR-88D S-band radar data in the United States showed that using this model significantly improved nowcasts of first-flash lightning occurrence versus the traditional weather radar-based ice mass estimator as well as using lightning flash-rate density directly.</p>

scholarly journals The large-scale spatio-temporal variability of precipitation over Sweden observed from the weather radar network

2013 ◽  
Vol 6 (6) ◽  
pp. 10699-10730
Author(s):  
A. Devasthale ◽  
L. Norin
Keyword(s):  
Temporal Variability ◽  
High Intensity ◽  
Large Scale ◽  
Weather Radar ◽  
Radar Data ◽  
The Arctic ◽  
Northern Sweden ◽  
Late Night ◽  
Spatio Temporal ◽  
Precipitation Events

Abstract. Using measurements from the national network of 12 weather radar stations for the last decade (2000–2010), we investigate the large-scale spatio-temporal variability of precipitation over Sweden. These statistics provide useful information to evaluate regional climate models as well as for hydrology and energy applications. A strict quality control is applied to filter out noise and artifacts from the radar data. We focus on investigating four distinct aspects namely, the diurnal cycle of precipitation and its seasonality, the dominant time scale (diurnal vs. seasonal) of variability, precipitation response to different wind directions, and the correlation of precipitation events with the North Atlantic Oscillation (NAO) and the Arctic Oscillation (AO). When classified based on their intensity, moderate to high intensity events (precipitation > 0.34 mm (3 h)−1) peak distinctly during late afternoon over the majority of radar stations in summer and during late night or early morning in winter. Precipitation variability is highest over the southwestern parts of Sweden. It is shown that the high intensity events (precipitation > 1.7mm (3 h)−1) are positively correlated with NAO and AO (esp. over northern Sweden), while the low intensity events are negatively correlated (esp. over southeastern parts). It is further observed that southeasterly winds often lead to intense precipitation events over central and northern Sweden, while southwesterly winds contribute most to the total accumulated precipitation for all radar stations. Apart from its operational applications, the present study demonstrates the potential of the weather radar data set for studying climatic features of precipitation over Sweden.

scholarly journals AutoNowP: An Approach Using Deep Autoencoders for Precipitation Nowcasting Based on Weather Radar Reflectivity Prediction

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1653
Author(s):  
Gabriela Czibula ◽  
Andrei Mihai ◽  
Alexandra-Ioana Albu ◽  
Istvan-Gergely Czibula ◽  
Sorin Burcea ◽  
...  
Keyword(s):  
Binary Classification ◽  
Weather Radar ◽  
Predictive Performance ◽  
Weather Conditions ◽  
Radar Data ◽  
Probability Of Detection ◽  
Radar Reflectivity ◽  
Classification Model ◽  
Precipitation Forecast ◽  
Short Term

Short-term quantitative precipitation forecast is a challenging topic in meteorology, as the number of severe meteorological phenomena is increasing in most regions of the world. Weather radar data is of utmost importance to meteorologists for issuing short-term weather forecast and warnings of severe weather phenomena. We are proposing AutoNowP, a binary classification model intended for precipitation nowcasting based on weather radar reflectivity prediction. Specifically, AutoNowP uses two convolutional autoencoders, being trained on radar data collected on both stratiform and convective weather conditions for learning to predict whether the radar reflectivity values will be above or below a certain threshold. AutoNowP is intended to be a proof of concept that autoencoders are useful in distinguishing between convective and stratiform precipitation. Real radar data provided by the Romanian National Meteorological Administration and the Norwegian Meteorological Institute is used for evaluating the effectiveness of AutoNowP. Results showed that AutoNowP surpassed other binary classifiers used in the supervised learning literature in terms of probability of detection and negative predictive value, highlighting its predictive performance.

scholarly journals Analysis of the Global Microwave Polarization Data of Clouds

2018 ◽  
Vol 32 (1) ◽  
pp. 3-13 ◽  
Author(s):  
Xiping Zeng ◽  
Gail Skofronick-Jackson ◽  
Lin Tian ◽  
Amber E. Emory ◽  
William S. Olson ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Radar Data ◽  
Ice Crystals ◽  
Ice Particles ◽  
Precipitation Measurement ◽  
Wave Imaging ◽  
Microwave Imager ◽  
Climate Analysis ◽  
Microwave Radiometers ◽  
Global Precipitation

Abstract Information about the characteristics of ice particles in clouds is necessary for improving our understanding of the states, processes, and subsequent modeling of clouds and precipitation for numerical weather prediction and climate analysis. Two NASA passive microwave radiometers, the satellite-borne Global Precipitation Measurement (GPM) Microwave Imager (GMI) and the aircraft-borne Conical Scanning Millimeter-Wave Imaging Radiometer (CoSMIR), measure vertically and horizontally polarized microwaves emitted by clouds (including precipitating particles) and Earth’s surface below. In this paper, GMI (or CoSMIR) data are analyzed with CloudSat (or aircraft-borne radar) data to find polarized difference (PD) signals not affected by the surface, thereby obtaining the information on ice particles. Statistical analysis of 4 years of GMI and CloudSat data, for the first time, reveals that optically thick clouds contribute positively to GMI PD at 166 GHz over all the latitudes and their positive magnitude of 166-GHz GMI PD varies little with latitude. This result suggests that horizontally oriented ice crystals in thick clouds are common from the tropics to high latitudes, which contrasts the result of Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) that horizontally oriented ice crystals are rare in optically thin ice clouds.

scholarly journals Toward Completing the Raindrop Size Spectrum: Case Studies Involving 2D-Video Disdrometer, Droplet Spectrometer, and Polarimetric Radar Measurements

2017 ◽  
Vol 56 (4) ◽  
pp. 877-896 ◽  
Author(s):  
Merhala Thurai ◽  
Patrick Gatlin ◽  
V. N. Bringi ◽  
Walter Petersen ◽  
Patrick Kennedy ◽  
...  
Keyword(s):  
Radar Data ◽  
Size Spectrum ◽  
Polarimetric Radar ◽  
Dual Frequency ◽  
Low Profile ◽  
Precipitation Measurement ◽  
Raindrop Size ◽  
Drop Size Distributions ◽  
Differential Reflectivity ◽  
Global Precipitation

AbstractAnalysis of drop size distributions (DSD) measured by collocated Meteorological Particle Spectrometer (MPS) and a third-generation, low-profile, 2D-video disdrometer (2DVD) are presented. Two events from two different regions (Greeley, Colorado, and Huntsville, Alabama) are analyzed. While the MPS, with its 50-μm resolution, enabled measurements of small drops, typically for drop diameters below about 1.1 mm, the 2DVD provided accurate measurements for drop diameters above 0.7 mm. Drop concentrations in the 0.7–1.1-mm overlap region were found to be in excellent agreement between the two instruments. Examination of the combined spectra clearly reveals a drizzle mode and a precipitation mode. The combined spectra were analyzed in terms of the DSD parameters, namely, the normalized intercept parameter NW, the mass-weighted mean diameter Dm, and the standard deviation of mass spectrum σM. The inclusion of small drops significantly affected the NW and the ratio σM/Dm toward higher values relative to using the 2DVD-based spectra alone. For each of the two events, polarimetric radar data were used to characterize the variation of radar-measured reflectivity Zh and differential reflectivity Zdr with Dm from the combined spectra. In the Greeley event, this variation at S band was well captured for small values of Dm (<0.5 mm) where measured Zdr tended to 0 dB but Zh showed a noticeable decrease with decreasing Dm. For the Huntsville event, an overpass of the Global Precipitation Measurement mission Core Observatory satellite enabled comparison of satellite-based dual-frequency radar retrievals of Dm with ground-based DSD measurements. Small differences were found between the satellite-based radar retrievals and disdrometers.

scholarly journals Evaluation of Radar Rainfall Products: Lessons Learned from the NASA TRMM Validation Program in Florida

2006 ◽  
Vol 23 (11) ◽  
pp. 1492-1505 ◽  
Author(s):  
Eyal Amitai ◽  
David A. Marks ◽  
David B. Wolff ◽  
David S. Silberstein ◽  
Brad L. Fisher ◽  
...  
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Product Evaluation ◽  
Radar Data ◽  
Lessons Learned ◽  
Limiting Factors ◽  
Radar Rainfall ◽  
Precipitation Measurement ◽  
Ground Validation ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Abstract Evaluation of the Tropical Rainfall Measuring Mission (TRMM) satellite observations is conducted through a comprehensive ground validation (GV) program. Since the launch of TRMM in late 1997, standardized instantaneous and monthly rainfall products are routinely generated using quality-controlled ground-based radar data adjusted to the gauge accumulations from four primary sites. As part of the NASA TRMM GV program, effort is being made to evaluate these GV products. This paper describes the product evaluation effort for the Melbourne, Florida, site. This effort allows us to evaluate the radar rainfall estimates, to improve the algorithms in order to develop better GV products for comparison with the satellite products, and to recognize the major limiting factors in evaluating the estimates that reflect current limitations in radar rainfall estimation. Lessons learned and suggested improvements from this 8-yr mission are summarized in the context of improving planning for future precipitation missions, for example, the Global Precipitation Measurement (GPM).

scholarly journals Multiscale assessment of TRMM (3B42 V7) and GPM (IMERG V5) satellite precipitation products over a Mediterranean mountainous watershed with sparse rain gauges in the Moroccan High Atlas (case study of Zat basin)

2021 ◽  
Author(s):  
Myriam Benkirane ◽  
Nour-Eddine Laftouhi ◽  
Said Khabba ◽  
Bouabid El Mansouri
Keyword(s):  
Probability Of Detection ◽  
Satellite Precipitation ◽  
Critical Success ◽  
Precipitation Measurement ◽  
Catchment Areas ◽  
Mountainous Watershed ◽  
Short Time ◽  
Global Precipitation ◽  
Trmm 3B42 ◽  
Precipitation Events

Abstract. The performance of Tropical Precipitation Measurement Mission (TRMM) and its successor, Global Precipitation Measurement (GPM), has provided hydrologists with a source of critical precipitation data for hydrological applications in basins where ground-based observations of precipitation are sparse, or spatially undistributed. The very high temporal and spatial resolution satellite precipitation products have therefore become a reliable alternative that researchers are increasingly using in various hydro-meteorological and hydro-climatological applications. This study aims to evaluate statistically and hydrologically the TRMM (3B42 V7) and GPM (IMERG V5) satellite precipitations products (SPPs), at multiple temporal scales from 2010 to 2017, in a mountainous watershed characterized by the Mediterranean climate. The results show that TRMM (3B42 V7) and GPM (IMERG V5) satellite precipitation products have a significant capacity for detecting precipitation at different time steps. However, the statistical analysis of SPPs against ground observation shows good results for both statistical metrics and contingency statistics with notable values (CC > 0.8), and representative values relatively close to 0 for the probability of detection (POD), critical success index (CSI), and false alarm ratio (FAR). Moreover, the sorting of the events implemented on the hydrological model was performed seasonally, at daily time steps. The calibrated episodes showed very good results with Nash values ranging from 53.2 % to 95.5 %. Nevertheless, the (IMERG V5) product detects more efficiently precipitation events at short time steps (daily), while (3B42 V7) has a strong ability to detect precipitation events at large time steps (monthly and yearly). Furthermore, the modeling results illustrate that both satellite precipitation products tend to underestimate precipitation during wet seasons and overestimate them during dry seasons, while they have a better spatial distribution of precipitation measurements performance, which shows the importance of their use for basin modeling and potentially for flood forecasting in Mediterranean catchment areas.

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