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This paper proposes a new quantitative precipitation estimation (QPE) technique to provide accurate rainfall estimates in complex terrain, where conventional QPE has limitations. The operational radar QPE in Taiwan is mainly based on the simplified relationship between radar reflectivity Z and rain rate R [R(Z) relation] and only utilizes the single-point lowest available echo to estimate rain rates, leading to low accuracy in complex terrain. Here, we conduct QPE using deep learning that extracts features from 3D radar reflectivities to address the above issues. Convolutional neural networks (CNN) are used to analyze contoured frequency by altitude diagrams (CFADs) to generate the QPE. CNN models are trained on existing rain gauges in northern and eastern Taiwan with the 3-yr data during 2015–17 and validated and tested using 2018 data. The weights of heavy rains (≥10 mm h−1) are increased in the model loss calculation to handle the unbalanced rainfall data and improve accuracy. Results show that the CNN outperforms the R(Z) relation based on the 2018 rain gauge data. Furthermore, this research proposes methods to conduct 2D gridded QPE at every pixel by blending estimates from various trained CNN models. Verification based on independent rain gauges shows that the CNN QPE solves the underestimation of the R(Z) relation in mountainous areas. Case studies are presented to visualize the results, showing that the CNN QPE generates better small-scale rainfall features and more accurate precipitation information. This deep learning QPE technique may be helpful for the disaster prevention of small-scale flash floods in complex terrain. [ABSTRACT FROM AUTHOR]

Radar rainfall nowcasting has mostly been applied to relatively large (often rural) domains (e.g., river basins), although rainfall nowcasting in small urban areas is expected to be more challenging. Here, we selected 80 events with high rainfall intensities (at least one 1-km2 grid cell experiences precipitation >15 mm h−1 for 1-h events or 30 mm day−1 for 24-h events) in five urban areas (Maastricht, Eindhoven, The Hague, Amsterdam, and Groningen) in the Netherlands. We evaluated the performance of 9060 probabilistic nowcasts with 20 ensemble members by applying the short-term ensemble prediction system (STEPS) from Pysteps to every 10-min issue time for the selected events. We found that nowcast errors increased with decreasing (urban) areas especially when below 100 km2. In addition, at 30-min lead time, the underestimation of nowcasts was 38% larger and the discrimination ability was 11% lower for 1-h events than for 24-h events. A set of gridded correction factors for the Netherlands, CARROTS (Climatology-based Adjustments for Radar Rainfall in an Operational Setting) could adjust the bias in real-time QPE and nowcasts by 70%. Yet, nowcasts were still found to underestimate rainfall more than 50% above 40-min lead time relative to the reference, which indicates that this error originates from the nowcasting model itself. Also, CARROTS did not adjust the rainfall spatial distribution in urban areas much. In summary, radar-based nowcasting for urban areas (between 67 and 213 km2) in the Netherlands exhibits a short skillful lead time of about 20 min, which can only be used for last-minute warning and preparation. [ABSTRACT FROM AUTHOR]

The National Aeronautics and Space Administration (NASA) and National Oceanic and Atmospheric Administration (NOAA) have a long and successful history of weather radar research. The NOAA ground-based radars—WSR-88D network—provide nationwide precipitation observations and estimates with advanced polarimetric capability. As a counterpart, the NASA–JAXA spaceborne radar—the Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM DPR)—has global coverage and higher vertical resolution than ground-based radars. While significant advances from both NOAA's WSR-88D network and NASA–JAXA's spaceborne radar DPR have been made, no systematic comparisons between the WSR-88D network and the DPR have been done. This study for the first time generates nationwide comprehensive comparisons at 136 WSR-88D radar sites from 2014 to 2020. Systematic differences in reflectivity are found, with ground radar reflectivity on average 2.4 dB smaller than that of the DPR (DPR version 6). This research found the discrepancies between WSR-88D and DPR arise from different calibration standards, signal attenuation correction, and differences in the ground and spaceborne scattering volumes. The recently updated DPR version 7 product improves rain detection and attenuation corrections, effectively reducing the overall average WSR-88D and DPR reflectivity differences to 1.0 dB. The goal of this study is to examine the systematic differences of radar reflectivity between the NOAA WSR-88D network and the NASA–JAXA DPR and to draw attention to radar-application users in recognizing their differences. Further investigation into understanding and alleviating the systematic bias between the two platforms is needed. [ABSTRACT FROM AUTHOR]

Convective processes are critical in the northernmost section of the Andes, where highly complex orography combines with tropical dynamics to produce intense precipitating systems. Data from satellite and ground radar have shown that Colombia's central Magdalena valley has a marked precipitation diurnal cycle, with high rainfall rates produced by deep convective events that intensify on the foothills of the surrounding valley. This study aims to contribute to the understanding of the orographic controls imposed on the convective activity observed in this important tropical valley. The atmospheric environments associated with convective events are assessed using WRF high-resolution simulations for a two-week period that includes intense rainfall events and marked dry spells. In addition, a simulation with modified orography is evaluated to analyze further the effect of topography in controlling the observed convective systems. The results suggest that northward and southward wind regimes channeled by the valley's topography and cross-valley flow result in a robust nocturnal convergence zone, which is associated with the strong diurnal cycle and the valley configuration. Furthermore, northerly low-level flow is an important source of moisture for convection in the valley. Additionally, results show midlevel tropospheric heating enhancement during days with more rainfall produced by convective systems in the western slope of the valley. The modified topography simulation suggests that circulation and precipitation in the valley are strongly dependent on the actual terrain features. [ABSTRACT FROM AUTHOR]

This study examined the impact of assimilating 3D temperature and water vapor information in addition to radar observations in a multiscale weather system. A frontal system with extremely heavy rainfall over northern Taiwan was selected. Using the WRF–LETKF Radar Assimilation System, we performed three sets of observing system simulation experiments to assimilate radar observations with or without thermodynamic variables obtained using different methods. First, assimilating the radar data for 2 h showed better structure and short-term forecast than 1 h. Second, we assimilated radar data and thermodynamic variables from a perfect model simulation. The results of the analysis revealed that when a precipitation position error occurred in the background field, assimilating thermodynamic information with the radar data could correct the dynamic structure and shorten the spinup assimilation period, resulting in substantial improvements to the quantitative precipitation forecast. Third, we applied a thermodynamics retrieval algorithm for a feasibility study. With a warm and wet bias of the retrieved fields, assimilating the temperature data had significant impact on the midlevel of stratiform areas and the forecast of the heavy rainfall was consequently improved. Assimilating the water vapor information helped reconstruct the range and intensity of the cold pool, but the improvement of rainfall forecast was limited. The optimal results of analysis and short-term forecast were achieved when both retrieved temperature and water vapor fields were assimilated. In conclusion, assimilating thermodynamic variables in the precipitation system is feasible for shortening the spinup period of data assimilation and improving the analysis and short-term forecast. [ABSTRACT FROM AUTHOR]

Isolated warm-rain cells are an important feature over the tropical oceans. Although warm rain is typically associated with relatively small raindrops, large raindrops (>4.5 mm in diameter) have been observed in some cases. Previous studies have examined warm rain cells with large drops on a case-study basis, but they have yet to be investigated in a broader, statistical sense. During the recent Propagation of Intraseasonal Oscillations (PISTON) field campaign, a C-band polarimetric radar routinely measured extreme values of differential reflectivity in small, isolated convection, indicating the presence of large drops. Using an objective feature identification and tracking algorithm, this study offers new insights to the structure and frequency of cells containing large drops. Cells with high differential reflectivity (>3.5 dB) were present in 24% of all radar scans. The cells were typically small (8-km2 mean area), short lived (usually <10 min), and shallow (3.7-km mean height). High differential reflectivity was more often found on the upwind side of the cells, suggesting a size sorting mechanism was operating establishing a low concentration of large drops on the upwind side. Differential reflectivity also tended to increase at lower altitudes, which is hypothesized to be due to continued drop growth and increasing temperature (increasing the dielectric constant of water). Rapid vertical cross-section radar scans, as well as transects made by a Learjet aircraft with onboard particle probes, are also used to analyze these cells, and support the conclusions drawn from statistical analysis. [ABSTRACT FROM AUTHOR]

Meso-γ-scale (2–20 km) local heavy rain (LHR) can cause fatalities through the sudden rise of rivers and flooding of roads. To help prevent this loss of life, we developed prediction methods for these types of meteorological hazards. We assimilated ground-based cloud radar (Ka-band radar) data that can capture cloud droplets before raindrops form and attempted to predict LHR with a cloud resolving numerical weather prediction (NWP) model. High-temporal (1-min interval) three-dimensional cloud radar data obtained through special observation were assimilated using a water vapor nudging method in the pre-rain stage of an LHR-causing cumulonimbus. While rainfall was not predicted by the NWP model without assimilation, LHR was predicted approximately 20 min after the conclusion of cloud radar data assimilation cycling. Results suggest that NWP with cloud radar data assimilation in the pre-rain stage has great potential for predicting LHR, and can lead to an early evacuation warning and subsequent evacuation of vulnerable populations. Significance Statement: The development of prediction methods for local (within several kilometers) heavy rain (LHR) is important because LHR events can cause deaths through the sudden rise of rivers and flooding of roads by rapidly developing (≤30 min) rain clouds. This study aims to develop a method for predicting LHR even before it begins to rain, which has been difficult to date. Using a technique called data assimilation, which integrates observation and simulation, we developed a method for assimilating cloud radar observations that can capture cloud droplets before raindrops form. As a result, we succeeded in predicting LHR before rainfall commenced. By extending and applying this research, early evacuation of vulnerable populations during LHR is possible. [ABSTRACT FROM AUTHOR]

There is a great need for gridded daily precipitation datasets to support a wide variety of disciplines in science and industry. Production of such datasets faces many challenges, from station data ingest to gridded dataset distribution. The quality of the dataset is directly related to its information content, and each step in the production process provides an opportunity to maximize that content. The first opportunity is maximizing station density from a variety of sources and assuring high quality through intensive screening, including manual review. To accommodate varying data latency times, the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) Climate Group releases eight versions of a day's precipitation grid, from 24 h after day's end to 6 months of elapsed time. The second opportunity is to distribute the station data to a grid using methods that add information and minimize the smoothing effect of interpolation. We use two competing methods, one that utilizes the information in long-term precipitation climatologies, and the other using weather radar return patterns. Last, maintaining consistency among different time scales (monthly vs daily) affords the opportunity to exploit information available at each scale. Maintaining temporal consistency over longer time scales is at cross purposes with maximizing information content. We therefore produce two datasets, one that maximizes data sources and a second that includes only networks with long-term stations and no radar (a short-term data source). Further work is under way to improve station metadata, refine interpolation methods by producing climatologies targeted to specific storm conditions, and employ higher-resolution radar products. [ABSTRACT FROM AUTHOR]

Whether precipitation falls in the form of rain or snow is of great importance to glacier accumulation and ablation. Assessments of phase-aware precipitation have been lacking over the vast area of the Tibetan Plateau (TP) due to the scarcity of surface measurements and the low quality of satellite estimates in this region. In this study, we attempt a satellite radar-based method for this precipitation partition, in which the CloudSat radar is used for snowfall while the Global Precipitation Measurement Mission radar is used for rainfall estimation. Assuming that 11-yr snowfall and 5-yr rainfall estimates represent the mean states of precipitation at each phase, the phase partition characteristics, including its annual mean, spatial pattern, seasonal dependence, and variation with elevations, are then discussed. Averaged over the highland area (over 1 km above mean sea level) in TP, the annual total precipitation is estimated to be around 400 mm, of which about 40% falls as snow. The snowfall mass fraction is about 45% in the northern and 30% in the southern part of TP, and about 80% in the cold and 30% in the warm half of the year. Surface elevation is found to be a high-impact factor on total precipitation and its phase partition, generally with total precipitation decreasing but snowfall fraction increasing with the increase of elevation. While there are some shortcomings, the current approach in combining snowfall and rainfall estimates from two satellite radars presents a useful pathway to assessing phase-aware precipitation over the TP region. Significance Statement: Whether precipitation falls in the form of rain or snow is of great importance to glacier accumulation and ablation over the Tibetan Plateau region, and yet a plateau-wide assessment of the phase-resolved precipitation has been lacking due to the scarcity of surface measurements in this region. In this study, we attempt a satellite radar-based method to separately estimate liquid and solid phase precipitation. The phase-resolved precipitation characteristics, including its annual mean, spatial pattern, seasonal dependence, and variation with elevation, are then analyzed and discussed. To the authors' knowledge, this is the first satellite observation-based study to evaluate a plateau-wide, phase-resolved annual mean precipitation, and its dependence on season, location, and elevation. [ABSTRACT FROM AUTHOR]