Instantaneous Precipitation Rates Research Articles

A Deep Learning Multimodal Method for Precipitation Estimation

To improve precipitation estimation accuracy, new methods, which are able to merge different precipitation measurement modalities, are necessary. In this study, we propose a deep learning method to merge rain gauge measurements with a ground-based radar composite and thermal infrared satellite imagery. The proposed convolutional neural network, composed of an encoder–decoder architecture, performs a multiscale analysis of the three input modalities to estimate simultaneously the rainfall probability and the precipitation rate value with a spatial resolution of 2 km. The training of our model and its performance evaluation are carried out on a dataset spanning 5 years from 2015 to 2019 and covering Belgium, the Netherlands, Germany and the North Sea. Our results for instantaneous precipitation detection, instantaneous precipitation rate estimation, and for daily rainfall accumulation estimation show that the best accuracy is obtained for the model combining all three modalities. The ablation study, done to compare every possible combination of the three modalities, shows that the combination of rain gauges measurements with radar data allows for a considerable increase in the accuracy of the precipitation estimation, and the addition of satellite imagery provides precipitation estimates where rain gauge and radar coverage are lacking. We also show that our multi-modal model significantly improves performance compared to the European radar composite product provided by OPERA and the quasi gauge-adjusted radar product RADOLAN provided by the DWD for precipitation rate estimation.

Convective Precipitation Efficiency Observed in the Tropics

AbstractPrecipitation efficiency refers to the fraction of condensate in the atmosphere that reaches the surface as precipitation. A high‐quality data set of radar‐estimated precipitation rates and convective scale vertical velocity near Darwin, Australia, is used to construct the first estimate of precipitation efficiency at convective scales for a long record of observations in the tropics. It is found that precipitation efficiency increases with precipitation rate and midtropospheric humidity and decreases with increasing convective available potential energy and surface temperature. Precipitation efficiency is largest under moist monsoonal conditions and smallest during monsoon break periods, which are characterized by a drier free troposphere. However, these differences in efficiency do not translate to differences in the instantaneous precipitation rate across the synoptic regimes because of a compensating change in the net condensation rate. This is driven by variations in cloud updraft velocity, which is larger in drier environments than in moist environments.

Deep Learning for Precipitation Estimation from Satellite and Rain Gauges Measurements

This paper proposes a multimodal and multi-task deep-learning model for instantaneous precipitation rate estimation. Using both thermal infrared satellite radiometer and automatic rain gauge measurements as input, our encoder–decoder convolutional neural network performs a multiscale analysis of these two modalities to estimate simultaneously the rainfall probability and the precipitation rate value. Precipitating pixels are detected with a Probability Of Detection (POD) of 0.75 and a False Alarm Ratio (FAR) of 0.3. Instantaneous precipitation rate is estimated with a Root Mean Squared Error (RMSE) of 1.6 mm/h.

Latent Heat Nudging in the Canadian Regional Deterministic Prediction System

Abstract This study reports on the progress toward operational weather radar data assimilation in Canada. As a first step, the latent heat nudging (LHN) technique has been tested for a period of 1 month. It is the first time that LHN is used across the North American continent, a domain significantly larger than that of other LHN studies. Other novel aspects of this study include the use of a quality index associated with individual reflectivity measurements and a discussion on matching the effective resolution of the modeled precipitation for a reduction of the representation errors. Various verification scores indicate that LHN has a positive influence on instantaneous precipitation rates for lead times up to 3 h. In comparison, the nowcasting of precipitation rates by a simple Lagrangian extrapolation method yields improvements that last up to approximately 4 h. Verifications against aircraft measurements indicate small but statistically significant improvements throughout the troposphere for lead times up to 24 h.

The Instantaneous Retrieval of Precipitation Over Land by Temporal Variation at 19 GHz

The primary signal used in all current passive microwave precipitation retrieval algorithms over land is the depression of the instantaneous brightness temperature (TB) caused by ice scattering. This study presents a new methodology to retrieve instantaneous precipitation rate over land by using TB temporal variation (ΔTB) at 19 GHz, which primarily reflects the surface emissivity variation due to the precipitation impact. As a proof-of-concept, we exploit observations from five polar-orbiting satellites over the Southern Great Plains (SGP) of the United States. Results show that ΔTB at 19 GHz correlate well with the instantaneous precipitation rate. Further analysis shows that ΔTB at 19 GHz is better correlated with the precipitation rate when multiple satellite observations are used due to the much shorter re-visit time for a certain location. The retrieved instantaneous precipitation rate over SGP from ΔTB at 19 GHz reasonably agrees with the surface radar observations, with the correlation, the root mean square error and the bias being 0.49, 2.39 mm/hr and 6.54%, respectively. Future work seeks to combine the ice scattering signal at high frequencies and this surface emissivity variation signal at low frequencies to achieve an optimal retrieval performance.

Reprocessed, Bias-Corrected CMORPH Global High-Resolution Precipitation Estimates from 1998

Abstract The Climate Prediction Center (CPC) morphing technique (CMORPH) satellite precipitation estimates are reprocessed and bias corrected on an 8 km × 8 km grid over the globe (60°S–60°N) and in a 30-min temporal resolution for an 18-yr period from January 1998 to the present to form a climate data record (CDR) of high-resolution global precipitation analysis. First, the purely satellite-based CMORPH precipitation estimates (raw CMORPH) are reprocessed. The integration algorithm is fixed and the input level 2 passive microwave (PMW) retrievals of instantaneous precipitation rates are from identical versions throughout the entire data period. Bias correction is then performed for the raw CMORPH through probability density function (PDF) matching against the CPC daily gauge analysis over land and through adjustment against the Global Precipitation Climatology Project (GPCP) pentad merged analysis of precipitation over ocean. The reprocessed, bias-corrected CMORPH exhibits improved performance in representing the magnitude, spatial distribution patterns, and temporal variations of precipitation over the global domain from 60°S to 60°N. Bias in the CMORPH satellite precipitation estimates is almost completely removed over land during warm seasons (May–September), while during cold seasons (October–April) CMORPH tends to underestimate the precipitation due to the less-than-desirable performance of the current-generation PMW retrievals in detecting and quantifying snowfall and cold season rainfall. An intercomparison study indicated that the reprocessed, bias-corrected CMORPH exhibits consistently superior performance than the widely used TRMM 3B42 (TMPA) in representing both daily and 3-hourly precipitation over the contiguous United States and other global regions.

Resolution Dependence of Future Tropical Cyclone Projections of CAM5.1 in the U.S. CLIVAR Hurricane Working Group Idealized Configurations

AbstractThe four idealized configurations of the U.S. CLIVAR Hurricane Working Group are integrated using the global Community Atmospheric Model version 5.1 at two different horizontal resolutions, approximately 100 and 25 km. The publicly released 0.9° × 1.3° configuration is a poor predictor of the sign of the 0.23° × 0.31° model configuration’s change in the total number of tropical storms in a warmer climate. However, it does predict the sign of the higher-resolution configuration’s change in the number of intense tropical cyclones in a warmer climate. In the 0.23° × 0.31° model configuration, both increased CO2 concentrations and elevated sea surface temperature (SST) independently lower the number of weak tropical storms and shorten their average duration. Conversely, increased SST causes more intense tropical cyclones and lengthens their average duration, resulting in a greater number of intense tropical cyclone days globally. Increased SST also increased maximum tropical storm instantaneous precipitation rates across all storm intensities. It was found that while a measure of maximum potential intensity based on climatological mean quantities adequately predicts the 0.23° × 0.31° model’s forced response in its most intense simulated tropical cyclones, a related measure of cyclogenesis potential fails to predict the model’s actual cyclogenesis response to warmer SSTs. These analyses lead to two broader conclusions: 1) Projections of future tropical storm activity obtained by a direct tracking of tropical storms simulated by coarse-resolution climate models must be interpreted with caution. 2) Projections of future tropical cyclogenesis obtained from metrics of model behavior that are based solely on changes in long-term climatological fields and tuned to historical records must also be interpreted with caution.

Real-time correction of weather radar data for the effects of bright band, range and orographic growth in widespread precipitation

A new method of correcting data from the United Kingdom operational weather radar network is described. The physically based scheme is designed to produce estimates of instantaneous precipitation rate at the surface by compensating for the effects of the bright band, range and low-level orographic growth. In a preliminary study, the characteristic shape of the vertical profile of reflectivity factor was examined using a climatological dataset derived from range—height indication scans recorded by a high-resolution radar. The results were then used to construct an idealized reflectivity factor profile. In the correction procedure, the heights of significant turning points in the profile are diagnosed from relevant meteorological data at each radar pixel. The parametrized profile is weighted by the radar-beam power profile and the surface precipitation rate found by an iterative method in real-time. The scheme has some important advantages over the alternative correction strategies. Detailed quantitative evaluation in typical cases of mainly frontal rainfall over southern England suggested that root-mean-square errors in estimates of instantaneous precipitation rates over 5 km pixels were reduced to less than half compared to the same radar data subject to only a fixed range correction.

Real‐time correction of weather radar data for the effects of bright band, range and orographic growth in widespread precipitation

AbstractA new method of correcting data from the United Kingdom operational weather radar network is described. The physically based scheme is designed to produce estimates of instantaneous precipitation rate at the surface by compensating for the effects of the bright band, range and low‐level orographic growth. In a preliminary study, the characteristic shape of the vertical profile of reflectivity factor was examined using a climatological dataset derived from range—height indication scans recorded by a high‐resolution radar. The results were then used to construct an idealized reflectivity factor profile. In the correction procedure, the heights of significant turning points in the profile are diagnosed from relevant meteorological data at each radar pixel. The parametrized profile is weighted by the radar‐beam power profile and the surface precipitation rate found by an iterative method in real‐time. The scheme has some important advantages over the alternative correction strategies. Detailed quantitative evaluation in typical cases of mainly frontal rainfall over southern England suggested that root‐mean‐square errors in estimates of instantaneous precipitation rates over 5 km pixels were reduced to less than half compared to the same radar data subject to only a fixed range correction.