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Abstract
Microwave links can be used for the estimation of path-averaged rainfall by using either the path-integrated attenuation or the difference in attenuation of two signals with different frequencies and/or polarizations. Link signals have been simulated using measured time series of raindrop size distributions (DSDs) over a period of nearly 2 yr, in combination with wind velocity data and Taylor’s hypothesis. For this purpose, Taylor’s hypothesis has been tested using more than 1.5 yr of high-resolution radar data. In terms of correlation between spatial and temporal profiles of rainfall intensities, the validity of Taylor’s hypothesis quickly decreases with distance. However, in terms of error statistics, the hypothesis is seen to hold up to distances of at least 10 km. Errors and uncertainties (mean bias error and root-mean-square error, respectively) in microwave link rainfall estimates due to spatial DSD variation are at a minimum at frequencies (and frequency combinations) where the power-law relation for the conversion to rainfall intensity is close to linear. Errors generally increase with link length, whereas uncertainties decrease because of the decrease of scatter about the retrieval relations because of averaging of spatially variable DSDs for longer links. The exponent of power-law rainfall retrieval relations can explain a large part of the variation in both bias and uncertainty, which means that the order of magnitude of these error statistics can be predicted from the value of this exponent, regardless of the link length.
Abstract
Microwave links can be used for the estimation of path-averaged rainfall by using either the path-integrated attenuation or the difference in attenuation of two signals with different frequencies and/or polarizations. Link signals have been simulated using measured time series of raindrop size distributions (DSDs) over a period of nearly 2 yr, in combination with wind velocity data and Taylor’s hypothesis. For this purpose, Taylor’s hypothesis has been tested using more than 1.5 yr of high-resolution radar data. In terms of correlation between spatial and temporal profiles of rainfall intensities, the validity of Taylor’s hypothesis quickly decreases with distance. However, in terms of error statistics, the hypothesis is seen to hold up to distances of at least 10 km. Errors and uncertainties (mean bias error and root-mean-square error, respectively) in microwave link rainfall estimates due to spatial DSD variation are at a minimum at frequencies (and frequency combinations) where the power-law relation for the conversion to rainfall intensity is close to linear. Errors generally increase with link length, whereas uncertainties decrease because of the decrease of scatter about the retrieval relations because of averaging of spatially variable DSDs for longer links. The exponent of power-law rainfall retrieval relations can explain a large part of the variation in both bias and uncertainty, which means that the order of magnitude of these error statistics can be predicted from the value of this exponent, regardless of the link length.
Abstract
Applications like drought monitoring and forecasting can profit from the global and near-real-time availability of satellite-based precipitation estimates once their related uncertainties and challenges are identified and treated. To this end, this study evaluates the IMERG V06B Late Run precipitation product from the Global Precipitation Measurement mission (GPM), a multisatellite product that combines space-based radar, passive microwave (PMW), and infrared (IR) data into gridded precipitation estimates. The evaluation is performed on the spatiotemporal resolution of IMERG (0.1° × 0.1°, 30 min) over the Netherlands over a 5-yr period. A gauge-adjusted radar precipitation product from the Royal Netherlands Meteorological Institute (KNMI) is used as reference, against which IMERG shows a large positive bias. To find the origin of this systematic overestimation, the data are divided into seasons, rainfall intensity ranges, echo top height (ETH) ranges, and categories based on the relative contributions of IR, morphing, and PMW data to the IMERG estimates. Furthermore, the specific radiometer is identified for each PMW-based estimate. IMERG’s detection performance improves with higher ETH and rainfall intensity, but the associated error and relative bias increase as well. Severe overestimation occurs during low-intensity rainfall events and is especially linked to PMW observations. All individual PMW instruments show the same pattern: overestimation of low-intensity events and underestimation of high-intensity events. IMERG misses a large fraction of shallow rainfall events, which is amplified when IR data are included. Space-based retrieval of shallow and low-intensity precipitation events should improve before IMERG can become accurate over the middle and high latitudes.
Abstract
Applications like drought monitoring and forecasting can profit from the global and near-real-time availability of satellite-based precipitation estimates once their related uncertainties and challenges are identified and treated. To this end, this study evaluates the IMERG V06B Late Run precipitation product from the Global Precipitation Measurement mission (GPM), a multisatellite product that combines space-based radar, passive microwave (PMW), and infrared (IR) data into gridded precipitation estimates. The evaluation is performed on the spatiotemporal resolution of IMERG (0.1° × 0.1°, 30 min) over the Netherlands over a 5-yr period. A gauge-adjusted radar precipitation product from the Royal Netherlands Meteorological Institute (KNMI) is used as reference, against which IMERG shows a large positive bias. To find the origin of this systematic overestimation, the data are divided into seasons, rainfall intensity ranges, echo top height (ETH) ranges, and categories based on the relative contributions of IR, morphing, and PMW data to the IMERG estimates. Furthermore, the specific radiometer is identified for each PMW-based estimate. IMERG’s detection performance improves with higher ETH and rainfall intensity, but the associated error and relative bias increase as well. Severe overestimation occurs during low-intensity rainfall events and is especially linked to PMW observations. All individual PMW instruments show the same pattern: overestimation of low-intensity events and underestimation of high-intensity events. IMERG misses a large fraction of shallow rainfall events, which is amplified when IR data are included. Space-based retrieval of shallow and low-intensity precipitation events should improve before IMERG can become accurate over the middle and high latitudes.
Abstract
We investigate the spatiotemporal structure of rainfall at spatial scales from 7 m to over 200 km in the Netherlands. We used data from two networks of laser disdrometers with complementary interstation distances in two Dutch cities (comprising five and six disdrometers, respectively) and a Dutch nationwide network of 31 automatic rain gauges. The smallest aggregation interval for which raindrop size distributions were collected by the disdrometers was 30 s, while the automatic rain gauges provided 10-min rainfall sums. This study aims to supplement other micro-γ investigations (usually performed in the context of spatial rainfall variability within a weather radar pixel) with new data, while characterizing the correlation structure across an extended range of scales. To quantify the spatiotemporal variability, we employ a two-parameter exponential model fitted to the spatial correlograms and characterize the parameters of the model as a function of the temporal aggregation interval. This widely used method allows for a meaningful comparison with seven other studies across contrasting climatic settings all around the world. We also separately analyzed the intermittency of the rainfall observations. We show that a single parameterization, consisting of a two-parameter exponential spatial model as a function of interstation distance combined with a power-law model for decorrelation distance as a function of aggregation interval, can coherently describe rainfall variability (both spatial correlation and intermittency) across a wide range of scales. Limiting the range of scales to those typically found in micro-γ variability studies (including four of the seven studies to which we compare our results) skews the parameterization and reduces its applicability to larger scales.
Abstract
We investigate the spatiotemporal structure of rainfall at spatial scales from 7 m to over 200 km in the Netherlands. We used data from two networks of laser disdrometers with complementary interstation distances in two Dutch cities (comprising five and six disdrometers, respectively) and a Dutch nationwide network of 31 automatic rain gauges. The smallest aggregation interval for which raindrop size distributions were collected by the disdrometers was 30 s, while the automatic rain gauges provided 10-min rainfall sums. This study aims to supplement other micro-γ investigations (usually performed in the context of spatial rainfall variability within a weather radar pixel) with new data, while characterizing the correlation structure across an extended range of scales. To quantify the spatiotemporal variability, we employ a two-parameter exponential model fitted to the spatial correlograms and characterize the parameters of the model as a function of the temporal aggregation interval. This widely used method allows for a meaningful comparison with seven other studies across contrasting climatic settings all around the world. We also separately analyzed the intermittency of the rainfall observations. We show that a single parameterization, consisting of a two-parameter exponential spatial model as a function of interstation distance combined with a power-law model for decorrelation distance as a function of aggregation interval, can coherently describe rainfall variability (both spatial correlation and intermittency) across a wide range of scales. Limiting the range of scales to those typically found in micro-γ variability studies (including four of the seven studies to which we compare our results) skews the parameterization and reduces its applicability to larger scales.
Rainfall-Induced Attenuation Correction for Two Operational Dual-Polarization C-Band Radars in the NetherlandsAbstract
The Royal Netherlands Meteorological Institute (KNMI) operates two operational dual-polarization C-band weather radars providing 2D radar rainfall products. Attenuation can result in severe underestimation of rainfall amounts, particularly in convective situations that are known to have high impact on society. To improve the radar-based precipitation estimates, two attenuation correction methods are evaluated and compared: 1) modified Kraemer (MK) method, i.e., Hitschfeld–Bordan where parameters of the power-law Z h –k h relation are adjusted such that reflectivities in the entire dataset do not exceed 59 dBZ h and attenuation correction is limited to 10 dB; and 2) using two-way path-integrated attenuation computed from the dual-polarization moment specific differential phase K dp (Kdp method). In both cases the open-source Python library wradlib is employed for the actual attenuation correction. A radar voxel only contributes to the computed path-integrated attenuation if its height is below the forecasted freezing-level height from the numerical weather prediction model HARMONIE-AROME. The methods are effective in improving hourly and daily quantitative precipitation estimation (QPE), where the Kdp method performs best. The verification against rain gauge data shows that the underestimation diminishes from 55% to 37% for hourly rainfall for the Kdp method when the gauge indicates more than 10 mm of rain in that hour. The improvement for the MK method is less pronounced, with a resulting underestimation of 40%. The stability of the MK method holds a promise for application to data archives from single-polarization radars.
Abstract
The Royal Netherlands Meteorological Institute (KNMI) operates two operational dual-polarization C-band weather radars providing 2D radar rainfall products. Attenuation can result in severe underestimation of rainfall amounts, particularly in convective situations that are known to have high impact on society. To improve the radar-based precipitation estimates, two attenuation correction methods are evaluated and compared: 1) modified Kraemer (MK) method, i.e., Hitschfeld–Bordan where parameters of the power-law Z h –k h relation are adjusted such that reflectivities in the entire dataset do not exceed 59 dBZ h and attenuation correction is limited to 10 dB; and 2) using two-way path-integrated attenuation computed from the dual-polarization moment specific differential phase K dp (Kdp method). In both cases the open-source Python library wradlib is employed for the actual attenuation correction. A radar voxel only contributes to the computed path-integrated attenuation if its height is below the forecasted freezing-level height from the numerical weather prediction model HARMONIE-AROME. The methods are effective in improving hourly and daily quantitative precipitation estimation (QPE), where the Kdp method performs best. The verification against rain gauge data shows that the underestimation diminishes from 55% to 37% for hourly rainfall for the Kdp method when the gauge indicates more than 10 mm of rain in that hour. The improvement for the MK method is less pronounced, with a resulting underestimation of 40%. The stability of the MK method holds a promise for application to data archives from single-polarization radars.
Abstract
A method for the operational monitoring of the weather radar antenna mechanics and signal processing is presented. The method is based on the analysis of sun signals in the polar volume data produced during the operational scanning of weather radars. Depending on the hardware of the radar, the volume coverage pattern, the season, and the latitude of the radar, several tens of sun hits are found per day. The method is an extension of that for determining the weather radar antenna pointing and for monitoring the receiver stability and the differential reflectivity offset. In the method the width of the sun image in elevation and in azimuth is analyzed from the data, together with the center point position and the total power, analyzed in the earlier methods. This paper describes how the width values are obtained in the majority of cases without affecting the quality of the position and power values. Results from the daily analysis reveal signal processing features and failures that are difficult to find out otherwise in weather data. Moreover, they provide a tool for monitoring the stability of the antenna system, and hence the method has great potential for routine monitoring of radar signal processing and the antenna mechanics. Hence, it is recommended that the operational solar analysis be extended into the analysis of the width.
Abstract
A method for the operational monitoring of the weather radar antenna mechanics and signal processing is presented. The method is based on the analysis of sun signals in the polar volume data produced during the operational scanning of weather radars. Depending on the hardware of the radar, the volume coverage pattern, the season, and the latitude of the radar, several tens of sun hits are found per day. The method is an extension of that for determining the weather radar antenna pointing and for monitoring the receiver stability and the differential reflectivity offset. In the method the width of the sun image in elevation and in azimuth is analyzed from the data, together with the center point position and the total power, analyzed in the earlier methods. This paper describes how the width values are obtained in the majority of cases without affecting the quality of the position and power values. Results from the daily analysis reveal signal processing features and failures that are difficult to find out otherwise in weather data. Moreover, they provide a tool for monitoring the stability of the antenna system, and hence the method has great potential for routine monitoring of radar signal processing and the antenna mechanics. Hence, it is recommended that the operational solar analysis be extended into the analysis of the width.
Abstract
Precipitation nowcasting is essential for weather-dependent decision-making, but it remains a challenging problem despite active research. The combination of radar data and deep learning methods has opened a new avenue for research. Radar data are well suited for precipitation nowcasting due to the high space–time resolution of the precipitation field. On the other hand, deep learning methods allow the exploitation of possible nonlinearities in the precipitation process. Thus far, deep learning approaches have demonstrated equal or better performance than optical flow methods for low-intensity precipitation, but nowcasting high-intensity events remains a challenge. In this study, we have built a deep generative model with various extensions to improve nowcasting of heavy precipitation intensities. Specifically, we consider different loss functions and how the incorporation of temperature data as an additional feature affects the model’s performance. Using radar data from KNMI and 5–90-min lead times, we demonstrate that the deep generative model with the proposed loss function and temperature feature outperforms other state-of-the-art models and benchmarks. Our model, with both loss function and feature extensions, is skillful at nowcasting precipitation for high rainfall intensities, up to 60-min lead time.
Abstract
Precipitation nowcasting is essential for weather-dependent decision-making, but it remains a challenging problem despite active research. The combination of radar data and deep learning methods has opened a new avenue for research. Radar data are well suited for precipitation nowcasting due to the high space–time resolution of the precipitation field. On the other hand, deep learning methods allow the exploitation of possible nonlinearities in the precipitation process. Thus far, deep learning approaches have demonstrated equal or better performance than optical flow methods for low-intensity precipitation, but nowcasting high-intensity events remains a challenge. In this study, we have built a deep generative model with various extensions to improve nowcasting of heavy precipitation intensities. Specifically, we consider different loss functions and how the incorporation of temperature data as an additional feature affects the model’s performance. Using radar data from KNMI and 5–90-min lead times, we demonstrate that the deep generative model with the proposed loss function and temperature feature outperforms other state-of-the-art models and benchmarks. Our model, with both loss function and feature extensions, is skillful at nowcasting precipitation for high rainfall intensities, up to 60-min lead time.
