Stratiform Precipitation Events Research Articles

An analysis of the National Water Model for a mid-Atlantic suburban watershed

ABSTRACT Discharge values from the National Water Model (NWM) were compared to USGS stream gage discharge observations for the suburban Red Clay Creek watershed (drainage area ~140 km2 and mixed land-use), in Pennsylvania and Delaware, from 2016 to 2018. 18-hour retrospective simulations from the NWM were used with concurrent hourly USGS discharge observations from three locations along the Red Clay Creek. Results indicate that the mean of discharge estimates from the NWM and from USGS observations significantly differed and that the NWM generally underestimates low-flow conditions and overestimates high-flow conditions. Watershed size also impacted NWM performance (with performance degrading in smaller watersheds). A meteorological analysis determined that convective rainfall events were associated with 66% of the largest differences between NWM discharge estimates and USGS observations while mid-latitude cyclone stratiform precipitation events accounted for the other 34%. Lastly, of the largest 15 differences between the NWM and observations, 13 occurred with pre-cursor soil moisture that was below the mean (dry soil conditions), in conjunction with heavy rainfall. Given the NWM’s recent operational implementation, and its status as Prototype guidance, the results of this study present specific geographical and climatological findings that can aid in the NWM’s continued validation and improvement for similar regions.

A Conditional Generative Adversarial Network for Weather Radar Beam Blockage Correction

Missing or low-quality data regions usually happen to weather radars. One of the most common situations is beam blockage or partial beam blockage. Therefore, correction of weather radar observations that are partially or fully blocked is an indispensable step in radar data quality control and subsequent quantitative applications, especially in complex terrain environments such as the western United States. In this article, we propose a deep learning framework based on generative adversarial networks (GANs) for restoring partial beam blockage regions in polarimetric radar observations using local and global contextual information. Due to the diverse precipitation types, blockage conditions, and ground information in different areas, two radars deployed in two different regions characterized by different precipitation types are used to demonstrate the proposed methodology. Both are S-band operational Weather Surveillance Radar – 1988 Doppler (WSR-88D): KFWS located in Fort Worth, northern Texas, and KDAX located in Davis, northern California. For training the GAN model, this article simulates the partial beam blockage situations by manually cropping observation sectors of both KDAX and KFWS radar data. The trained models were tested using independent precipitation events in Texas and California to demonstrate the model effectiveness in inpainting “missing" data. In addition, this paper cross-tested the data with different precipitation features to examine the generalization capacity of the beam blockage correction models. The beam blockage correction performance is also compared with a traditional linear interpolation approach. The results show that for both domains the continuity of precipitation observations is greatly improved after applying the deep learning-based inpainting approach. For the KFWS test data, some visible discrepancies exist between the results from models trained based on convective and stratiform precipitation events in Texas and California, respectively, yet both models outperform the traditional interpolation method. For the KDAX test data, both the model trained using the KFWS data from convective precipitation events in Texas and the model trained using KDAX data from stratiform precipitation events in California render a similar performance. Although ground truth is not available for the real blocked radar data, the repaired observations demonstrated a great potential for improved quantitative applications.

Improved spectral processing for a multi-mode pulse compression Ka–Ku-band cloud radar system

Abstract. Cloud radars are widely used in observing clouds and precipitation. However, the raw data products of cloud radars are usually affected by multiple factors, which may lead to misinterpretation of cloud and precipitation processes. In this study, we present a Doppler-spectra-based data processing framework to improve the data quality of a multi-mode pulse-compressed Ka–Ku radar system. Firstly, non-meteorological signal close to the ground was identified with enhanced Doppler spectral ratios between different observing modes. Then, for the Doppler spectrum affected by the range sidelobe due to the implementation of the pulse compression technique, the characteristics of the probability density distribution of the spectral power were used to identify the sidelobe artifacts. Finally, the Doppler spectra observations from different modes were merged via the shift-then-average approach. The new radar moment products were generated based on the merged Doppler spectrum data. The presented spectral processing framework was applied to radar observations of a stratiform precipitation event, and the quantitative evaluation shows good performance of clutter or sidelobe suppression and spectral merging.

Vertical Structures Associated with Orographic Precipitation during Warm Season in the Sichuan Basin and Its Surrounding Areas at Different Altitudes from 8-Year GPM DPR Observations

Global precipitation measurement (GPM) is one of the effective means employed to observe orographic precipitation, and its inversed GPM DPR data can be used to study the microphysical structure of precipitation particles. This study considers statistics on convective precipitation (CP) and stratiform precipitation (SP) events over three types of terrain (plains, mountains, and high mountains) using the DPR onboard the GPM Core Observatory from May to September of 2014–2021 to analyze the vertical structure of heavy CP and SP. In mountain areas and high mountain areas, the updraft rendered by topography or seeder-feeder mechanism is not only conducive to the collision and merger of raindrops into large raindrops, but also increases the concentration of small drops, which is the main reason why the occurrence probability of not only large but also small raindrops increases and the horizontal distribution domain of mass weighted average raindrop diameter (Dm) widens. For heavy SP, the occurrence probability of medium-diameter precipitation particles below the freezing height (FzH) over high mountains is greater than those over plains. The precipitation particles above 10 km altitude of high mountains have characteristics, such as lower droplet number concentration and larger diameter, compared with those over plains. Furthermore, the study also analyzed the correlation between storm top altitude (STA) and Dm, water vapor and STA respectively. This study is helpful to further understand the effect of topography on heavy precipitation through cloud microphysical processes and the vertical structure of precipitation.

Retrievals of ice microphysical properties using dual-wavelength polarimetric radar observations during stratiform precipitation events

Abstract. Ice growth processes within clouds affect the type and amount of precipitation. Hence, the importance of an accurate representation of ice microphysics in numerical weather and numerical climate models has been confirmed by several studies. To better constrain ice processes in models, we need to study ice cloud regions before and during monitored precipitation events. For this purpose, two radar instruments facing each other were used to collect complementary measurements. The C-band POLDIRAD weather radar from the German Aerospace Center (DLR) in Oberpfaffenhofen and the Ka-band MIRA-35 cloud radar from the Ludwig Maximilians University of Munich (LMU) were used to monitor stratiform precipitation in the vertical cross-sectional area between the two instruments. The logarithmic difference of radar reflectivities at two different wavelengths (54.5 and 8.5 mm), known as the dual-wavelength ratio, was exploited to provide information about the size of the detected ice hydrometeors, taking advantage of the different scattering behavior in the Rayleigh and Mie regime. Along with the dual-wavelength ratio, differential radar reflectivity measurements from POLDIRAD provided information about the apparent shape of the detected ice hydrometeors. Scattering simulations using the T-matrix method were performed for oblate and horizontally aligned prolate ice spheroids of varying shape and size using a realistic particle size distribution and a well-established mass–size relationship. The combination of dual-wavelength ratio, radar reflectivity, and differential radar reflectivity measurements as well as scattering simulations was used for the development of a novel retrieval for ice cloud microphysics. The development of the retrieval scheme also comprised a method to estimate the hydrometeor attenuation in both radar bands. To demonstrate this approach, a feasibility study was conducted on three stratiform snow events which were monitored over Munich in January 2019. The ice retrieval can provide ice particle shape, size, and mass information which is in line with differential radar reflectivity, dual-wavelength ratio, and radar reflectivity observations, respectively, when the ice spheroids are assumed to be oblates and to follow the mass–size relation of aggregates. When combining two spatially separated radars to retrieve ice microphysics, the beam width mismatch can locally lead to significant uncertainties. However, the calibration uncertainty is found to cause the largest bias for the averaged retrieved size and mass. Moreover, the shape assumption is found to be equally important to the calibration uncertainty for the retrieved size, while it is less important than the calibration uncertainty for the retrieval of ice mass. A further finding is the importance of the differential radar reflectivity for the particle size retrieval directly above the MIRA-35 cloud radar. Especially for that observation geometry, the simultaneous slantwise observation from the polarimetric weather radar POLDIRAD can reduce ambiguities in retrieval of the ice particle size by constraining the ice particle shape.

Dark/bright band of a melting layer detected by coherent Doppler lidar and micro rain radar

Observation of a melting layer using a 1.55 µm coherent Doppler lidar (CDL) is first presented during a stratiform precipitation event. Simultaneous radar measurements are also performed by co-located 1.24 cm micro rain radar (MRR) and 10.6 cm Doppler weather radar (DWR). As a well-known bright band in radar reflectivity appears during precipitation, an interesting dark band about 160 m below that in lidar backscattering is observed. Due to the absorption effect, the backscattering from raindrops at 1.55 µm is found much weaker than that at short wavelengths usually used in direct detection lidars. However, the CDL provides additional Doppler information which is helpful for melting layer identification. For example, a spectrum bright band with broadened width and sign conversion of skewness is detected in this case. After a deep analysis of the power spectra, the aerosol and precipitation components are separated. The fall speed of hydrometeors given by CDL is found smaller than that of MRR, with the differences of approximately 0.5 m/s and 1.5 m/s for the snow and rainfall, respectively. To illustrate the influence of absorption effect, simulations of the backscatter coefficient and extinction coefficient of aerosol and rainfall are also performed at the wavelength range of 0.3 ∼ 2.2 µm using the Mie theory.

Separation of convective and stratiform precipitation using polarimetric radar data with a support vector machine method

Abstract. A precipitation separation approach using a support vector machine method was developed and tested on a C-band polarimetric weather radar located in Taiwan (RCMK). Different from those methods requiring whole-volume scan data, the proposed approach utilizes polarimetric radar data from the lowest unblocked tilt to classify precipitation echoes into either stratiform or convective types. In this algorithm, inputs of radar reflectivity, differential reflectivity, and the separation index are integrated through a support vector machine. The weight vector and bias in the support vector machine were optimized using well-classified data from two precipitation events. The proposed approach was tested with three precipitation events, including a widespread mixed stratiform and convective event, a tropical typhoon precipitation event, and a stratiform-precipitation event. Results from the multi-radar–multi-sensor (MRMS) precipitation classification algorithm were used as the ground truth in the performance evaluation. The performance of the proposed approach was also compared with the approach using the separation index only. It was found that the proposed method can accurately classify the convective and stratiform precipitation and produce better results than the approach using the separation index only.

Simulations of Microphysics and Precipitation in a Stratiform Cloud Case over Northern China: Comparison of Two Microphysics Schemes

Using the Weather Research and Forecasting (WRF) model with two different microphysics schemes, the Predicted Particle Properties (P3) and the Morrison double-moment parameterizations, we simulated a stratiform rainfall event on 20-21 April 2010. The simulation output was compared with precipitation and aircraft observations. The aircraft-observed moderate-rimed dendrites and plates indicated that riming contributed significantly to ice particle growth at the mature precipitation stage. Observations of dendrite aggregation and capped columns suggested that aggregation coexisted with deposition or riming and played an important role in producing many large particles. The domain-averaged values of the 24-h surface precipitation accumulation from the two schemes were quite close to each other. However, differences existed in the temporal and spatial evolutions of the precipitation distribution. An analysis of the surface precipitation temporal evolution indicated faster precipitation in Morrison, while P3 indicated slower rainfall by shifting the precipitation pattern eastward toward what was observed. The differences in precipitation values between the two schemes were related to the cloud water content distribution and fall speeds of rimed particles. P3 simulated the stratiform precipitation event better as it captured the gradual transition in the mass-weighted fall speeds and densities from unrimed to rimed particles.

Polarimetric Radar Variables in the Layers of Melting and Dendritic Growth at X Band—Implications for a Nowcasting Strategy in Stratiform Rain

AbstractTime series of quasi–vertical profiles (QVPs) from 52 stratiform precipitation events observed with the polarimetric X-band radar in Bonn, Germany (BoXPol), between 2013 and 2016 have been statistically analyzed to infer microphysical processes shaping the dendritic-growth-layer (DGL) and melting-layer (ML) signatures including surface rainfall. Specific differential phase KDP in the ML shows an average correlation of 0.65 with surface rainfall for these cases. Radar reflectivity decreases below the ML by about 2 dB on average while differential reflectivity ZDR is hardly affected, which suggests rain evaporation as the dominating effect. Estimated ice water content or snow water equivalent precipitation rate S in the DGL is correlated with surface rain rates with lead times of 30 min and longer, which opens a pathway for radar-based nowcasting of stratiform precipitation tendencies. Trajectories of snow generated aloft down to the surface are constructed from wind profiles derived both from the nearest radiosounding and radar-based velocity azimuth displays (VAD) to narrow down the location at which the DGL-generated snow reaches the surface as rain. The lagged correlation analysis between KDP in the DGL and reflectivity ZH at that location demonstrates the superiority of the VAD information. Correlation coefficients up to 0.80 with lead times up to 120 min provide a proof of concept for future nowcasting applications that are based on DGL monitoring. Statistical relations found in our QVP dataset provide a database for estimating intrinsic polarimetric variables from the usual azimuth and elevation scans within and in the vicinity of the ML.

Isotopic composition of precipitation during strong El Niño–Southern Oscillation events in the Southeast Region of Brazil

AbstractEquatorial Pacific sea surface temperature variations interact with processes of atmospheric circulation, creating conditions for the occurrence of El Niño–Southern Oscillation (ENSO). ENSO events represent the most important interannual phenomena affecting climate patterns worldwide and causing significant socio‐economic impacts. In the Brazilian territory, ENSO leads to an increase in drought episodes in the north‐eastern region and an increase in precipitation in the southern region, whereas the effects over the south‐east region are yet not well understood. The main goal of this study is to compare variations of isotopic composition in precipitation across the south‐east portion of the Brazilian territory during two very strong ENSO events: 1997–1998 (ENSO 1) and 2014–2016 (ENSO 2). Daily isotopic records, available from the Global Network of Isotopes in Precipitation database for ENSO 1, and samples collected during ENSO 2 were used to compare the influence of both events on the isotopic composition of precipitation. Seasonal variations indicated more depleted precipitation during the wet seasons (δ18O = −5.4 ± 4.0‰) and enriched precipitation during the dry seasons (δ18O = −2.8 ± 2.3‰). Observed rainfall variations were associated with atmospheric large‐scale processes and moisture transport from the Amazon region, whereas extreme values (enriched or depleted) appear to be associated with particular convective and stratiform precipitation events. Overall, more depleted isotopic composition of precipitation (δ18O = −4.60‰) and higher d‐excess (up to +15‰) were observed during the dry season of ENSO 1 when compared with ENSO 2 dry season (δ18O‰ = −2.80‰, d‐excess lower than +14‰). The latter is explained by greater atmospheric moisture content, particularly associated with recycling of transpiration fluxes from the Amazon region, during dry season of ENSO 1. No significant differences for δ18O and δ2H were observed during the wet season; however, d‐excess from ENSO 2 was greater than ENSO 1, due to the slightly greater atmospheric moisture content and very strong upward motion observed. Our findings highlight the opportunity that environmental isotopes offer towards understanding hydrometeorological processes, particularly, the evolution of extreme climatic events of global resonance such as ENSO.

Microphysical processes of a stratiform precipitation event over eastern China: analysis using micro rain radar data

Data collected using the micro rain radar (MRR) situated in Jinan city, eastern China, were used to explore the altitudinal and temporal evolution of rainfall microphysical characteristics, and to analyze the bright band (BB) characteristics and hydrometeor classification. Specifically, a low-intensity and stable stratiform precipitation event that occurred from 0000 to 0550 UTC 15 February 2015 and featured a BB was studied. During this event, the rainfall intensity was less than 2 mm h−1 at a height of 300 m, which was above the radar site level, so the errors caused by the vertical air motion could be ignored. The freezing height from the radiosonde matched well with the top of the BB observed by the MRR. It was also found that the number of 0.5–1 mm diameter drops showed no noticeable variation below the BB. The maximum fall velocity and the maximum gradient fall velocity (GFV) of the raindrops appeared at the bottom of the BB. Meanwhile, a method that uses the GFV and reflectivity to identify the altitude and the thickness of the BB was established, with which the MRR can provide a reliable and real-time estimation of the 0°C isotherm. The droplet fall velocity was used to classify the types of snow crystals above the BB. In the first 20 min of the selected precipitation event, graupel prevailed above the BB; and at an altitude of 2000 m, graupel also dominated in the first 250 min. After 150 min, the existence of graupel and dendritic crystals with water droplets above the BB was inferred.

Similarities and Improvements of GPM Dual-Frequency Precipitation Radar (DPR) upon TRMM Precipitation Radar (PR) in Global Precipitation Rate Estimation, Type Classification and Vertical Profiling

Spaceborne precipitation radars are powerful tools used to acquire adequate and high-quality precipitation estimates with high spatial resolution for a variety of applications in hydrological research. The Global Precipitation Measurement (GPM) mission, which deployed the first spaceborne Ka- and Ku-dual frequency radar (DPR), was launched in February 2014 as the upgraded successor of the Tropical Rainfall Measuring Mission (TRMM). This study matches the swath data of TRMM PR and GPM DPR Level 2 products during their overlapping periods at the global scale to investigate their similarities and DPR’s improvements concerning precipitation amount estimation and type classification of GPM DPR over TRMM PR. Results show that PR and DPR agree very well with each other in the global distribution of precipitation, while DPR improves the detectability of precipitation events significantly, particularly for light precipitation. The occurrences of total precipitation and the light precipitation (rain rates < 1 mm/h) detected by GPM DPR are ~1.7 and ~2.53 times more than that of PR. With regard to type classification, the dual-frequency (Ka/Ku) and single frequency (Ku) methods performed similarly. In both inner (the central 25 beams) and outer swaths (1–12 beams and 38–49 beams) of DPR, the results are consistent. GPM DPR improves precipitation type classification remarkably, reducing the misclassification of clouds and noise signals as precipitation type “other” from 10.14% of TRMM PR to 0.5%. Generally, GPM DPR exhibits the same type division for around 82.89% (71.02%) of stratiform (convective) precipitation events recognized by TRMM PR. With regard to the freezing level height and bright band (BB) height, both radars correspond with each other very well, contributing to the consistency in stratiform precipitation classification. Both heights show clear latitudinal dependence. Results in this study shall contribute to future development of spaceborne radar precipitation retrievals and benefit hydrological and meteorological research.

Monitoring the Absolute Calibration of a Polarimetric Weather Radar

AbstractThe absolute calibration of a dual-polarization radar of the German Weather Service is continuously monitored using the operational birdbath scan and collocated disdrometer measurements at the Hohenpeissenberg observatory. The goal is to measure the radar reflectivity constant Z better than ±1 dB. The assumption is that a disdrometer measurement close to the surface can be related to the radar measurement at the first far-field range bin. This is verified using a Micro Rain Radar (MRR). The MRR data fill the gap between the measurement near the surface and the far-field range bin at 650 m. Using data from the first half of the warm season in 2014, a bias in radar calibration of 1.8 dB is found. Data from only stratiform precipitation events are considered. After adjusting the radar calibration and using an independent data sample, very good agreement is found between the radar, the MRR, and the disdrometer with a bias in smaller than 1 dB. The bias in is not captured with the classic one-point calibration, which is performed twice a day using a built-in test signal generator. This is attributed to the fact that the characterization of the transmit and receive path is not accurate enough. Solar interferences during the operational scanning are used to characterize the receiver. There, the bias found is small, about 0.2 dB, so that bias based on the comparison of the radar with external sensors is attributed to the transmit path. The representativeness of the disdrometer measurements are assessed using two additional disdrometers located within 200-m distance.

Estimation of snowfall limit for the Kashmir Valley, Indian Himalayas, with TRMM PR Bright Band information

Knowing the height of the snowfall limit during precipitation events is crucial for better understanding a number of hydro-climatic processes, for instance glacier-climate interactions or runoff from high mountain catchments. However, knowledge on heights of the phase change during precipitation events is limited by the small number of meteorological measurements available at high altitudes, such as the Himalayas. The bright band (BB) of satellite based radar data may be a promising proxy for the snow/rain transition during particular stratiform precipitation events over high mountain regions. The BB is a horizontal layer of stronger radar reflectivity caused by the melting of hydrometeors at the level where solid precipitation turns into rain. Here, we present BB heights detected by the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) 2A23 algorithm over a mountainous area. To assess the performance of BB heights, we have compared a 17-year data set of BB estimations of the TRMM PR with radiosonde observations and meteorological station data from Srinagar, Kashmir Valley, India. During March to November, the BB lies mostly about 200 to 800m below the freezing level (FL) recorded by radiosondes. The correlation between BB and FL heights extrapolated from a ground-based station is smaller and depends on the timing of the air temperature measurement – an important finding for applying extrapolation techniques in data sparse regions. Further on, we found a strong seasonal and monthly variability of the BB height, e.g. extending in summer months from about 2700m to almost 6000m asl. Comparison with near surface rain intensity from the TRMM PR product 2A25 indicates that – during intense monsoonal summer precipitation events – the BB height is concentrated between about 3500 and 4000m asl. We can conclude that TRMM PR BB data deliver valuable complementary information for regional or seasonal variability in snow/rain transition in data sparse regions and, further on, BB data from surrounding lowlands could be used to validate extrapolation approaches to assess snowfall limit for mainly stratiform precipitation events where stations at high elevations are missing.

Polarimetric radar and aircraft observations of saggy bright bands during MC3E

AbstractPolarimetric radar observations increasingly are used to understand cloud microphysical processes, which is critical for improving their representation in cloud and climate models. In particular, there has been recent focus on improving representations of ice collection processes (e.g., aggregation and riming), as these influence precipitation rate, heating profiles, and ultimately cloud life cycles. However, distinguishing these processes using conventional polarimetric radar observations is difficult, as they produce similar fingerprints. This necessitates improved analysis techniques and integration of complementary data sources. The Midlatitude Continental Convective Clouds Experiment (MC3E) provided such an opportunity. Quasi‐vertical profiles of polarimetric radar variables in two MC3E stratiform precipitation events reveal episodic melting layer sagging. Integrated analyses using scanning and vertically pointing radar and aircraft measurements reveal that saggy bright band signatures are produced when denser, faster‐falling, more isometric hydrometeors (relative to adjacent times) descend into the melting layer. In one case, strong circumstantial evidence for riming is found during bright band sagging times. A bin microphysical melting layer model successfully reproduces many aspects of the signature, supporting the observational analysis. If found to be a reliable indicator of riming, saggy bright bands could be a proxy for the presence of supercooled liquid water in stratiform precipitation, which may provide important information for mitigating aircraft icing risks and for constraining microphysical models.

Operational C-Band Dual-Polarization Radar QPE for the Subtropical Complex Terrain of Taiwan

Complex terrain poses significant challenges to the radar based quantitative precipitation estimation (QPE) because of blockages to the lower tilts of radar observations. The blockages often force the use of higher tilts data to estimate precipitation at the ground and result in errors due to vertical variations of the radar variables. To obtain accurate radar QPEs in the subtropical complex terrain of Taiwan, a vertically corrected composite algorithm (VCCA) was developed for two C-band polarimetric radars. The new algorithm corrects higher tilt radar variables with the vertical profile of reflectivity (VPR) or vertical profile of specific differential phase (VPSDP) and estimates rainfall rate at the ground through an automated combination ofR-ZandR-KDPrelations. The VCCA was assessed with three precipitation cases of different regimes including typhoon, mei-yu, and summer stratiform precipitation events. The results showed that a combination ofR-ZandR-KDPrelations provided more accurate QPEs than each alone becauseR-Zprovides better rainfall estimates for light rains andR-KDPrelation is more suitable for heavy rains. The vertical profile corrections for reflectivity and specific differential phase significantly reduced radar QPE errors caused by inadequate sampling of the orographic enhancement of precipitation near the ground.

Temporal and spatial scaling impacts on extreme precipitation

Abstract. Convective and stratiform precipitation events have fundamentally different physical causes. Using a radar composite over Germany, this study separates these precipitation types and compares extremes at different spatial and temporal scales, ranging from 1 to 50 km and 5 min to 6 h, respectively. Four main objectives are addressed. First, we investigate extreme precipitation intensities for convective and stratiform precipitation events at different spatial and temporal resolutions to identify type-dependent space and time reduction factors and to analyze regional and seasonal differences over Germany. We find strong differences between the types, with up to 30% higher reduction factors for convective compared to stratiform extremes, exceeding all other observed seasonal and regional differences within one type. Second, we investigate how the differences in reduction factors affect the contribution of each type to extreme events as a whole, again dependent on the scale and the threshold chosen. A clear shift occurs towards more convective extremes at higher resolution or higher percentiles. For horizontal resolutions of current climate model simulations, i.e., ~10 km, the temporal resolution of the data as well as the chosen threshold have profound influence on which type of extreme will be statistically dominant. Third, we compare the ratio of area to duration reduction factor for convective and stratiform events and find that convective events have lower effective advection velocities than stratiform events and are therefore more strongly affected by spatial than by temporal aggregation. Finally, we discuss the entire precipitation distribution regarding data aggregation and identify matching pairs of temporal and spatial resolutions where similar distributions are observed. The information is useful for planning observational networks or storing model data at different temporal and spatial scales.

Intercomparisons of CloudSat and Ground-Based Radar Retrievals of Rain Rate over Land

AbstractExperimental retrievals of rain rates using the CloudSat spaceborne 94-GHz radar reflectivity gradient method over land were evaluated by comparing them with standard estimates from ground-based operational S-band radar measurements, which are widely used for quantitative precipitation estimations. The comparisons were performed for predominantly stratiform precipitation events that occurred in the vicinity of the Weather Surveillance Radar-1988 Doppler (WSR-88D) KGWX and KSHV radars during the CloudSat overpasses in the vicinity of these ground radar sites. The standard reflectivity-based WSR-88D rain-rate retrievals used in operational practice were utilized as a reference for the CloudSat retrieval evaluation. Spaceborne and ground-based radar rain-rate estimates that were closely collocated in space and time were generally well correlated. The correlation coefficients were approximately 0.65 on average, and the mean relative biases were usually within ±35% for the whole dataset and for individual events with typical rain rates exceeding ~2 mm h−1. For events with lighter rainfall, higher biases and lower correlations were often present. The normalized mean absolute differences between satellite- and ground-based radar retrievals were on average ~60%, with an increasing trend for lighter rainfall. Such mean differences are comparable to combined retrieval errors from both ground-based and satellite radar remote sensing approaches. Evaluation of potential effects of partial beam blockage on the ground-based radar measurements was performed, and the influence of the choice of relation between WSR-88D reflectivity and rain rate that was utilized in the ground-based rain-rate retrievals was assessed.

Incorporating NASA Spaceborne Radar Data into NOAA National Mosaic QPE System for Improved Precipitation Measurement: A Physically Based VPR Identification and Enhancement Method

AbstractThis study proposes an approach that identifies and corrects for the vertical profile of reflectivity (VPR) by using Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) measurements in the region of Arizona and southern California, where the ground-based Next Generation Weather Radar (NEXRAD) finds difficulties in making reliable estimations of surface precipitation amounts because of complex terrain and limited radar coverage. A VPR identification and enhancement (VPR-IE) method based on the modeling of the vertical variations of the equivalent reflectivity factor using a physically based parameterization is employed to obtain a representative VPR at S band from the TRMM PR measurement at Ku band. Then the representative VPR is convolved with ground radar beam sampling properties to compute apparent VPRs for enhancing NEXRAD quantitative precipitation estimation (QPE). The VPR-IE methodology is evaluated with several stratiform precipitation events during the cold season and is compared to two other statistically based correction methods, that is, the TRMM PR–based rainfall calibration and a range ring–based adjustment scheme. The results show that the VPR-IE has the best overall performance and provides much more accurate surface rainfall estimates than the original ground-based radar QPE. The potential of the VPR-IE method could be further exploited and better utilized when the Global Precipitation Measurement Mission's dual-frequency PR is launched in 2014, with anticipated accuracy improvements and expanded latitude coverage.

A regional ensemble forecast system for stratiform precipitation events in the Northern China Region. Part II: Seasonal evaluation for summer 2010

In this study, the Institute of Atmospheric Physics, Chinese Academy of Sciences — regional ensemble forecast system (IAP-REFS) described in Part I was further validated through a 65-day experiment using the summer season of 2010. The verification results show that IAP-REFS is skillful for quantitative precipitation forecasts (QPF) and probabilistic QPF, but it has a systematic bias in forecasting near-surface variables. Applying a 7-day running mean bias correction to the forecasts of near-surface variables remarkably improved the reliability of the forecasts. In this study, the perturbation extraction and inflation method (proposed with the single case study in Part I) was further applied to the full season with different inflation factors. This method increased the ensemble spread and improved the accuracy of forecasts of precipitation and near-surface variables. The seasonal mean profiles of the IAP-REFS ensemble indicate good spread among ensemble members and some model biases at certain vertical levels.