Dense Gauge Network Research Articles

Exploring patterns in precipitation intensity–duration–area–frequency relationships using weather radar data

Abstract. Accurate estimations of extreme precipitation return levels are critical for many hydrological applications. Extreme precipitation is highly variable in both space and time; therefore, to better understand and manage the related risks, knowledge of their probability at different spatial–temporal scales is crucial. We employ a novel non-asymptotic framework to estimate extreme return levels (up to 100 years) at multiple spatial–temporal scales from weather radar precipitation estimates. The approach reduces uncertainties and enables the use of relatively short archives typical of weather radar data (12 years in this case). We focus on the eastern Mediterranean, an area of high interest due to its sharp climatic gradient, containing Mediterranean, semi-arid, and arid areas across a few tens of kilometres, and its susceptibility to flash flood. At-site intensity–duration–area–frequency relations are derived from radar precipitation data at various scales (10 min–24 h, 0.25–500 km2) across the study area, using ellipses of varying axes and orientations to account for the spatial component of storms. We evaluate our analysis using daily rain gauge data over areas for which sufficiently dense gauge networks are available. We show that extreme return levels derived from radar precipitation data for 24 h and 100 km2 are generally comparable to those derived from averaging daily rain gauge data over a similar areal scale. We then analyse differences in multi-scale extreme precipitation over coastal, mountainous, and desert regions. Our study reveals that the power-law scaling relationship between precipitation and duration (simple scaling) weakens for increasing area sizes. This finding has implications for temporal downscaling. Additionally, precipitation intensity varies significantly for different area sizes at short durations but becomes more similar at long durations, suggesting that, in the region, areal reduction factors may not be necessary for computing return levels over long durations. Furthermore, the reverse orographic effect, which causes decreased precipitation for hourly and sub-hourly durations, diminishes for larger areas. Finally, we discuss the effects of orography and coastline proximity on extreme precipitation intensity over different spatial–temporal scales.

Long‐Term High‐Resolution Gauge Adjusted Satellite Rainfall Product Over India

AbstractThis study aims to create a 21‐year, high spatiotemporal resolution Global Satellite Mapping of Precipitation (GSMaP) rainfall product adjusted by rain gauge measurements over the Indian mainland and highlighted the importance of the Indian Meteorological Department (IMD) daily gridded rainfall to generate gauge adjusted GSMaP rainfall products over Indian landmass. The targeted resolutions of the GSMaP are hourly and 0.1° × 0.1°. The National Oceanic and Atmospheric Administration Climate Prediction Center daily gauge analysis (0.5° × 0.5°) and IMD daily gridded rainfall (0.25° × 0.25°) were utilized to generate long‐term rainfall products, GSMaP_CPC and GSMaP_IMD rainfall, respectively. After preliminary verification of the GSMaP_CPC and GSMaP_IMD rainfalls with IMD gauges, these rainfall products are evaluated for the Indian Summer Monsoon periods of 2000–2020 with comparisons of other gauge adjusted rainfall products such as the Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement final‐run. The results suggest GSMaP_IMD has a smaller root‐mean‐square difference (RMSD) and higher correlation than GSMaP_CPC, evaluated against independent rainfall products. In the 3‐hour mean analysis with spaceborne precipitation radar data, it is found that the value of RMSD decreases in GSMaP_IMD with respect to GSMaP_CPC throughout the day. The statistics against the hourly dense gauge network suggests that the GSMaP_IMD is more effective in capturing large spatiotemporal rainfall variation. Thus, validation results with the independent sources suggest that GSMaP_IMD rainfall generally improved over GSMaP_CPC rainfall. These improvements are significant in orographic regions with high rainfall amounts, mainly the western Ghats and northeastern parts of India.

Reconstruction of Urban Rainfall Measurements to Estimate the Spatiotemporal Variability of Extreme Rainfall

In recent decades, the impact of climate change on urban flooding has increased, along with an increase in urban population and urban areas. Hence, historical design storms require revisions based on robust intensity–duration–frequency (IDF) relationships. To this end, the development of an urban rain-gauge network is essential to yield the spatiotemporal attributes of rainfall. The present study addresses two objectives: (a) to reconstruct sub-daily rainfall time series for the historical period over an urban gauge network, and (b) to investigate the spatiotemporal variation in extreme rainfall distribution within a city. This study considers Bangalore, India, where rainfall has been historically monitored by two stations but a dense gauge network has recently been developed. The study applies random forest regression for rainfall reconstruction, finding that the performance of the model is better when the predictand and predictor stations are near to one another. Robust IDF relationships confirm significant spatial variations in extreme rainfall distribution at the station and the city-region levels. The areal reduction factor (ARF) is also estimated in order to understand the likely impact of the reconstructed time series on hydrological modeling. A significant decrease in the ARF is observed as the area grows beyond 450 km2, indicating a substantial reduction in the volume of the design floods.

Understanding Intensity–Duration–Frequency (IDF) Curves Using IMERG Sub-Hourly Precipitation against Dense Gauge Networks

The design storm derived from intensity–duration–frequency (IDF) curves is the main input for hydrologic analysis or hydraulic design for flood control. The regions with higher flood risks due to extreme precipitation are often deficient in precipitation gauges. This study presents a detailed evaluation of IDF curves derived using IMERG Final half-hourly precipitation (V06), fitted with the widely used CDFs: Gumbel and MLE, Gumbel and MM, Pearson 3, and GEV. As benchmarks and following the same method, we also derived IDF curves using areal average gridded precipitation constructed from two dense gauges networks over (1) the WegenerNET Feldbach region in the Alpine forelands of Austria and (2) the gauge network of the Walnut Gulch Experimental Watershed, in a semiarid region of the United States. In both regions, the frequency analysis for return periods between 2 and 100 years was based on half-hourly rainfall and compared at a grid-scale with a spatial resolution of IMERG, 0.1° × 0.1° lat/lon. The impact of order in which the gridded gauge-based precipitation average is performed within an IMERG grid was evaluated by computing two different Annual Maximum Series (AMS). In one, the average was computed before obtaining the AMS (AB-AMS), and in the other, the average was computed after obtaining the AMS for each gauge grid (AA-AMS) within the IMERG grid. The evaluation revealed that IMERG AMS agrees better with AB-AMS than AA-AMS for the two study regions. Lastly, it was found that the use of Gumbel distribution in calculating IMERG IDF curves results in better agreement with the ground truth than the use of the other three distributions studied here. The outcomes should provide valuable knowledge for the application of IMERG precipitation over regions with sparse gauges.

Evaluation of GPM IMERG Rainfall Estimates in Singapore and Assessing Spatial Sampling Errors in Ground Reference

AbstractWe evaluated the Integrated Multisatellite Retrievals for GPM (IMERG) V06B Early and Final Run products using data from a dense gauge network in Singapore as ground reference (GR). The evaluation is carried out at monthly, daily, and hourly scales, and conditioned on different seasons and rainfall intensities. Further, different spatial configurations and densities of the gauge networks (3–17 gauges per IMERG cell) used here allowed us to examine spatial sampling errors (SSE) in the GR. The results revealed a probability of detection of 0.95 (0.65), critical success index of 0.69 (0.35), and a correlation of 0.60 (0.41) for the daily (hourly) scale. Results also indicate an overestimation of rainy days (hours) by IMERG compared to GR, leading to a false alarm ratio of 0.29 (0.57) at daily (hourly) scales. Analysis of probability distributions and conditional error metrics showed overestimation of lighter (0.2–4 mm day−1) and moderate (4–8 mm day−1) rainfall by IMERG, but better performance for heavier rainfall (≥32 mm day−1). The seasonal analysis showed improved performance of IMERG during November–February compared to June–September months. The hourly analysis further revealed large discrepancies in diurnal cycles during June–September. The SSE are studied in a Monte Carlo framework consisting of several synthetic networks with varying spatial configurations and densities. The effect of SSE on IMERG evaluation results is characterized following the error variance separation approach. For the gauge networks studied here, the contribution of SSE variance to IMERG daily error variance ranges from 4% to 24% depending on gauge spatial configuration, and is as large as 36% during intermonsoon months when rainfall is highly convective in nature.

A multi-site stochastic weather generator for high-frequency precipitation using censored skew-symmetric distribution

Stochastic weather generators (SWGs) are digital twins of complex weather processes and widely used in agriculture and urban design. Due to improved measuring instruments, an accurate SWG for high-frequency precipitation is now possible. However, high-frequency precipitation data are more zero-inflated, skewed, and heavy-tailed than common (hourly or daily) precipitation data. Therefore, classical methods that either model precipitation occurrence independently of their intensity or assume that the precipitation follows a censored meta-Gaussian process may not be appropriate. In this work, we propose a novel multi-site precipitation generator that drives both occurrence and intensity by a censored non-Gaussian vector autoregression model with skew-symmetric dynamics. The proposed SWG is advantageous in modeling skewed and heavy-tailed data with direct physical and statistical interpretations. We apply the proposed model to 30-second precipitation based on the data obtained from a dense gauge network in Lausanne, Switzerland. In addition to reproducing the high-frequency precipitation, the model can provide accurate predictions as the long short-term memory (LSTM) network but with uncertainties and more interpretable results.

Investigation of Orographic Precipitation over an Isolated, Three-Dimensional Complex Topography with a Dense Gauge Network, Radar Observations, and Upslope Model

Abstract This study uses a dense rain gauge network, radar observations, and an upslope model to document the detailed aspects of precipitation over Da-Tun Mountain (DT) of northern Taiwan under prevailing northeasterly monsoonal flow. DT is one of the most concentrated areas of heavy rainfall in Taiwan, with a size of ~15 km and a terrain peak of ~1 km (MSL), and it constitutes a concave ridge with two windward ridge arms that encompass a funnel-shaped valley. Twenty-one rainfall events identified over DT from January 2011 to February 2015 are chosen for analysis. More than half of the studied cases exhibit two local maxima of rainfall over the ridge arms, and asymmetric characteristics between these two maxima are evident. The other frequent rainfall pattern, observed as upstream winds are more from the east-northeast, is characterized by a local maximum of precipitation inside the valley. Analyses from the upslope model confirm that the occurrence of the windward maxima of rainfall is primarily caused by upslope lifting, and their asymmetric characteristics are closely related to the difference in the azimuthal variations of slope steepness between the two ridge arms. The observed characteristics of rainfall intensities inside the valley, however, cannot be well described by the upslope model. It is found that the lateral flow confluence induced by the deflected flows over the ridge arms may play an essential role in intensifying upslope-forced precipitation within the valley. This effect emerges as upstream winds are roughly parallel to the central axis of funneling regions located between the ridge arms.

Assessment of spatial uncertainty of heavy rainfall at catchment scale using a dense gauge network

Abstract. Hydrology and remote-sensing communities have made use of dense rain-gauge networks for studying rainfall uncertainty and variability. However, in most regions, these dense networks are only available at small spatial scales (e.g., within remote-sensing subpixel areas) and over short periods of time. Just a few studies have applied a similar approach, i.e., employing dense gauge networks to catchment-scale areas, which limits the verification of their results in other regions. Using 10-year rainfall measurements from a network of 150 rain gauges, WegenerNet (WEGN), we assess the spatial uncertainty in observed heavy rainfall events. The WEGN network is located in southeastern Austria over an area of 20 km × 15 km with moderate orography. First, the spatial variability in rainfall in the region was characterized using a correlogram at daily and sub-daily scales. Differences in the spatial structure of rainfall events between warm and cold seasons are apparent, and we selected heavy rainfall events, the upper 10 % of wettest days during the warm season, for further analyses because of their high potential for causing hazards. Secondly, we investigated the uncertainty in estimating mean areal rainfall arising from a limited gauge density. The average number of gauges required to obtain areal rainfall with errors less than a certain threshold (≤20 % normalized root-mean-square error – RMSE – is considered here) tends to increase, roughly following a power law as the timescale decreases, while the errors can be significantly reduced by establishing regularly distributed gauges. Lastly, the impact of spatial aggregation on extreme rainfall was examined, using gridded rainfall data with various horizontal grid spacings. The spatial-scale dependence was clearly observed at high intensity thresholds and high temporal resolutions; e.g., the 5 min extreme intensity increases by 44 % for the 99.9th and by 25 % for the 99th percentile, with increasing horizontal resolution from 0.1 to 0.01∘. Quantitative uncertainty information from this study can guide both data users and producers to estimate uncertainty in their own observational datasets, consequently leading to the sensible use of the data in relevant applications. Our findings could be transferred to midlatitude regions with moderate topography, but only to a limited extent, given that regional factors that can affect rainfall type and process are not explicitly considered in the study.

Ensemble Spatial Precipitation Analysis From Rain Gauge Data: Methodology and Application in the European Alps

AbstractThe deterministic nature of conventional precipitation data sets complicates their utility in applications. The underlying estimation principle (minimizing errors) involves biases in extremes, and conventional uncertainty quantification (cross‐validation) is impractical. We present a method to derive spatial analyses of daily precipitation, probabilistically, as an ensemble, conditional on the available rain gauge data. The method builds on and extends previous techniques using conditional simulation with Gaussian Random Fields. The extension involves trans‐Gaussian and piecewise covariance modeling to let the ensemble respond to regional precipitation conditions, Bayesian inference to account for parameter uncertainty, and simulation on a primary grid to ensure the ensemble has a well‐defined areal support. While addressing prevalent issues of existing techniques, our method involves a compromise in global consistency that limits the utility of the ensemble over large domains. We apply the method to derive ensembles of area‐mean precipitation for hydrological area units in the Alps. The ensembles are demonstrated to plausibly capture variations in uncertainty with rainfall condition, rain gauge density, and averaging area. Evaluations suggest that the probabilistic estimates are internally consistent and of good statistical reliability. There is a tendency to underestimate uncertainty for light precipitation. Results point to remarkable uncertainties, even with the dense gauge networks in the Alps: For means over 500‐km2 areas we find the ensemble spread to be typically a factor of 2–4 (factor of 1.5) for intense convective (stratiform) events. This also implies nonnegligible uncertainty in climate indices. Probabilistic representation of interpolation uncertainty in spatial data sets allows users to track them into applications.

Evaluation of TAPEER daily estimates and other GPM‐era products against dense gauge networks in West Africa, analysing ground reference uncertainty

A series of multi‐platform rainfall estimates is evaluated at the daily 1° resolution over three dense rain‐gauge networks in West Africa, in Niger, Burkina Faso and Benin, for the period 2012–2016. The quality of the ground reference, its dependence on the number of gauges and the consequences on the validation are discussed. The primary objective of this work is the evaluation of the newly released Tropical Analysis of Precipitation with an Estimation of Errors (TAPEER) daily one‐degree rainfall accumulation from the Megha‐Tropiques mission. Several other multi‐platform products, GSMAP, CMORPH, PERSIANN, TMPA and IMERG, are also evaluated over the same area to put TAPEER performance into perspective. For each product only the version that uses solely satellite information (no gauge adjustment) is used. The analysis concentrates on the heart of the rainy season in West Africa, from June to September. TAPEER exhibits good skill over the region, with a high correlation and a good reproduction of the frequency distribution of the rain rates, especially in the Sahelian region – Niger and Burkina Faso – where the biases are close to zero. The bias is stronger (and negative) in Benin. This overall good performance is stable from year to year. The other products exhibit less bias in Benin and more in the Sahel. CMORPH stands out with relatively good correlations, GSMAP slightly less and PERSIANN has a very strong bias in the Sahel. IMERG, analysed over a shorter period, is also promising.

Global intercomparison and regional evaluation of GPM IMERG Version-03, Version-04 and its latest Version-05 precipitation products: Similarity, difference and improvements

The overarching goal of this study is to intercompare the newly released Integrated Multi-satellitE Retrievals for GPM (IMERG) Version 05 (V05) products with its former Version 04 (V04) and Version 03 (V03) products and also assess any differences and improvements, with cross-evaluation against the Global Precipitation Climatology Project (GPCP) Version 2.3, Multi-Source Weighted-Ensemble Precipitation (MSWEP) Version 2.1 and the dense gauge networks in China. Firstly, the gauge-adjusted products (Final run) of V03, V04 and V05 are compared over the globe. Then, the near-real-time products without gauge adjustments (Early and Late run) and Final run products of all versions are evaluated against ground-based observations comprised of more than 30,000 gauges over Mainland China at 0.1° × 0.1° grid and hourly and daily temporal scales. The primary conclusions are: (1) globally, both V04 and V05 Final run show significant differences and improvements from V03. Particularly, the overall mean oceanic precipitation of V04 and V05 increases by +31.36% and +28.81% respectively from that of V03 and much closer to GPCP and MSWEP; (2) over Mainland China, the Early and Late run products of the same version (V03 or V04) generally have similar performance, while V04 Early and Late run have better performance in most regions than the corresponding run of V03 except in the arid Xinjiang Province and the mountainous Tibetan Plateau; and (3) V04 and V03 Final run show comparable performance, while V05 Final run generally improves upon both V04 and V03 and has the best performance among the seven standard IMERG products. The improvement of V05 Final run is particularly evident in southeastern and western China. At a timely matter, the study provides first-hand global and regional assessment feedback to IMERG algorithm developers and also sheds insights for GPM precipitation product users across the world.

Evaluation of Global Precipitation Measurement Rainfall Estimates against Three Dense Gauge Networks

Abstract Precipitation profiles from the Global Precipitation Measurement (GPM) Core Observatory Dual-Frequency Precipitation Radar (DPR; Ku and Ka bands) form part of the a priori database used in the Goddard profiling algorithm (GPROF) for retrievals of precipitation from passive microwave sensors, which are in turn used as high-quality precipitation estimates in gridded products. As GPROF performs precipitation retrievals as a function of surface classes, error characteristics may be dependent on surface types. In this study, the authors evaluate the rainfall estimates from DPR Ku as well as GPROF estimates from passive microwave sensors in the GPM constellation. The evaluation is conducted at the level of individual satellite pixels (5–15 km) against three dense networks of rain gauges, located over contrasting land surface types and rainfall regimes, with multiple gauges per satellite pixel and precise accumulation about overpass time to ensure a representative comparison. As expected, it was found that the active retrievals from DPR Ku generally performed better than the passive retrievals from GPROF. However, both retrievals struggle under coastal and semiarid environments. In particular, virga appears to be a serious challenge for both DPR Ku and GPROF. The authors detected the existence of lag due to the time it takes for satellite-observed precipitation to reach the ground, but the precise delay is difficult to quantify. It was also shown that subpixel variability is a contributor to the errors in GPROF. These results can pinpoint deficiencies in precipitation algorithms that may propagate into widely used gridded products.

Accounting for spatiotemporal errors of gauges: A critical step to evaluate gridded precipitation products

Rain gauge observations are commonly used to evaluate the quality of satellite precipitation products. However, the inherent difference between point-scale gauge measurements and areal satellite precipitation, i.e. a point of space in time accumulation v.s. a snapshot of time in space aggregation, has an important effect on the accuracy and precision of qualitative and quantitative evaluation results. This study aims to quantify the uncertainty caused by various combinations of spatiotemporal scales (0.1°–0.8° and 1–24 h) of gauge network designs in the densely gauged and relatively flat Ganjiang River basin, South China, in order to evaluate the state-of-the-art satellite precipitation, the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG). For comparison with the dense gauge network serving as “ground truth”, 500 sparse gauge networks are generated through random combinations of gauge numbers at each set of spatiotemporal scales. Results show that all sparse gauge networks persistently underestimate the performance of IMERG according to most metrics. However, the probability of detection is overestimated because hit and miss events are more likely fewer than the reference numbers derived from dense gauge networks. A nonlinear error function of spatiotemporal scales and the number of gauges in each grid pixel is developed to estimate the errors of using gauges to evaluate satellite precipitation. Coefficients of determination of the fitting are above 0.9 for most metrics. The error function can also be used to estimate the required minimum number of gauges in each grid pixel to meet a predefined error level. This study suggests that the actual quality of satellite precipitation products could be better than conventionally evaluated or expected, and hopefully enables non-subject-matter-expert researchers to have better understanding of the explicit uncertainties when using point-scale gauge observations to evaluate areal products.

China’s 1 km Merged Gauge, Radar and Satellite Experimental Precipitation Dataset

Based on high-density gauge precipitation observations, high-resolution weather radar quantitative precipitation estimation (QPE) and seamless satellite-based precipitation estimates, a 1-km experimental gauge-radar-satellite merged precipitation dataset has been developed using the proposed local gauge correction (LGC) and optimal interpolation (OI) merging strategies. First, hourly precipitation analyses from approximately 40,000 automatic weather stations at 0.01° resolution were used to correct bias in the radar QPE Group System (QPEGS), developed by the China Meteorological Administration (CMA) and the Climate Prediction Center Morphing (CMORPH) precipitation products. As precipitation events tend to have a more localized distribution at the hourly and 0.01° resolutions, three core parameters were improved using the OI method. (a) The spatial dependence of the error variance for radar QPE was accounted for over six sub-regions in China and is shown as a non-linear function of the gauge precipitation analysis. (b) The spatial dependence of error correlation for the radar QPE decreased exponentially with distance. (c) The error of the hourly gauge-based precipitation analysis was quantified as a function of the precipitation amount and the gauge network density, using the Monte Carlo method to randomly sample the gauge observations over the dense gauge network. The performance of the 1-km experimental gauge-radar-satellite merged precipitation dataset (named as China Merged Precipitation Analysis: CMPA_1km) was assessed at 6 h-temporal resolutions and 0.03° × 0.03° spatial resolution using precipitation observations from 208 independent hydrological stations as a reference. Compared with radar QPE and CMORPH, the CMPA-1km showed obviously better accuracy in all sub-regions and during all seasons. In contrast, gauge analysis and CMPA-1km shared similar accuracy, but the latter could estimate heavy precipitation more accurately than the former, as well as the latter has the advantage of seamless spatial coverage. However, the CMPA-1km exhibits larger uncertainty during the cold season compared to the warm season, which will need further improvement in future work. The downscaled bias-corrected 0.01° resolution CMORPH was employed to fill the gaps in regions, mainly in Western China and the Tibetan Plateau, where gauge and radar measurements are limited.

A Comparison between the GPM Dual-Frequency Precipitation Radar and Ground-Based Radar Precipitation Rate Estimates in the Swiss Alps and Plateau

Abstract The Global Precipitation Measurement (GPM) mission Dual-Frequency Precipitation Radar (DPR) provides a unique set of three-dimensional radar precipitation estimates across much of the globe. Both terrain and climatic conditions can have a strong influence on the reliability of these estimates. Switzerland provides an ideal testbed to evaluate the performance of the DPR in complex terrain: it consists of a mixture of very complex terrain (the Alps) and the far flatter Swiss Plateau. It is also well instrumented, covered with a dense gauge network as well as a network of four dual-polarization C-band weather radars, with the same instrument network used in both the Plateau and the Alps. Here an evaluation of the GPM DPR rainfall rate products against the MeteoSwiss radar rainfall rate product for the first two years of the GPM DPR’s operation is presented. Errors in both detection and estimation are considered, broken down by terrain complexity, season, precipitation phase, precipitation type, and precipitation rate. Errors are considered both integrated across the entire domain and spatially, and consistent underestimation of precipitation by GPM is found. This rises to −51% in complex terrain in the winter, primarily due to the predominance of DPR measurements wholly in the solid phase, where problems are caused by lower reflectivities. The smaller vertical extent of precipitation in winter is also likely a cause. Both detection and estimation performance are found to be significantly better in summer than in winter, in liquid than in solid precipitation, and in flatter terrain than in complex terrain.

Statistical and Hydrological Comparisons between TRMM and GPM Level-3 Products over a Midlatitude Basin: Is Day-1 IMERG a Good Successor for TMPA 3B42V7?

Abstract The goal of this study is to quantitatively intercompare the standard products of the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) and its successor, the Global Precipitation Measurement (GPM) mission Integrated Multisatellite Retrievals for GPM (IMERG), with a dense gauge network over the midlatitude Ganjiang River basin in southeast China. In general, direct comparisons of the TMPA 3B42V7, 3B42RT, and GPM Day-1 IMERG estimates with gauge observations over an extended period of the rainy season (from May through September 2014) at 0.25° and daily resolutions show that all three products demonstrate similarly acceptable (~0.63) and high (0.87) correlation at grid and basin scales, respectively, although 3B42RT shows much higher overestimation. Both of the post-real-time corrections effectively reduce the bias of Day-1 IMERG and 3B42V7 to single digits of underestimation from 20+% overestimation of 3B42RT. The Taylor diagram shows that Day-1 IMERG and 3B42V7 are comparable at grid and basin scales. Hydrologic assessment with the Coupled Routing and Excess Storage (CREST) hydrologic model indicates that the Day-1 IMERG product performs comparably to gauge reference data. In many cases, the IMERG product outperforms TMPA standard products, suggesting a promising prospect of hydrologic utility and a desirable hydrologic continuity from TRMM-era product heritages to GPM-era IMERG products. Overall, this early study highlights that the Day-1 IMERG product can adequately substitute TMPA products both statistically and hydrologically, even with its limited data availability to date, in this well-gauged midlatitude basin. As more IMERG data are released, more studies to explore the potential of GPM-era IMERG in water, weather, and climate research are urgently needed.

Precipitation Estimation from the ARM Distributed Radar Network during the MC3E Campaign

AbstractThis study presents radar-based precipitation estimates collected during the 2-month U.S. Department of Energy Atmospheric Radiation Measurement Program (ARM)–NASA Midlatitude Continental Convective Clouds Experiment (MC3E). Emphasis is on the usefulness of radar observations from the C-band and X-band scanning ARM precipitation radars (CSAPR and XSAPR, respectively) for rainfall estimation products to distances within 100 km of the Lamont, Oklahoma, ARM facility. The study utilizes a dense collection of collocated ARM, NASA Global Precipitation Measurement, and nearby surface Oklahoma Mesonet gauge records to evaluate radar-based hourly rainfall products and campaign-optimized methods over individual gauges and for areal rainfall characterizations. Rainfall products are also evaluated against the performance of a regional NWS Weather Surveillance Radar-1988 Doppler (WSR-88D) S-band dual-polarization radar product. Results indicate that the CSAPR system may achieve similar point– and areal–gauge bias and root-mean-square (RMS) error performance to a WSR-88D reference for the variety of MC3E deep convective events sampled. The best campaign rainfall performance was achieved when using radar relations capitalizing on estimates of the specific attenuation from the CSAPR system. The XSAPRs demonstrate limited capabilities, having modest success in comparison with the WSR-88D reference for hourly rainfall accumulations that are under 10 mm. All rainfall estimation methods exhibit a reduction by a factor of 1.5–2.5 in RMS errors for areal accumulations over a 15-km2 NASA dense gauge network, with the smallest errors typically associated with dual-polarization radar methods.

MODIS Land Surface Temperature as an index of surface air temperature for operational snowpack estimation

Regional operational modeling systems that support forecasters for the real-time warning of flash flood events often suffer from lack of adequate real-time surface air temperature data to force their accumulation and ablation snow model. The Land Surface Temperature (LST) product from MODIS, which provides four instantaneous readings per day, was tested for its feasibility to be used in real-time to derive spatially distributed surface air temperature (Ta) forcing for the operational snow model. The study was conducted in the Southeast region of Turkey using an atypically dense network of hourly Ta, daily snow depth, snow water equivalent (SWE), and rainfall datasets for the period: October 2002–September 2010. A comparison between the Ta and the corresponding LST grid-cell data indicated close associations that are different in nature for periods with and without snow on the ground. The LST-derived Ta was compared with that obtained from on-site gauge-based interpolation procedures and climatological time series. The LST-derived Ta was found inferior only to the Ta derived from the interpolation of the dense gauge network (31-gauges). Snow-pack simulations using estimated Ta time series were compared to simulations that were forced by the observed Ta at each site of 18 sites. The LST-derived Ta performed well in simulating snow mass and maximum SWE magnitude, while it did not represent well the timing of the annual peak of SWE and the duration of spring melt. Our study concluded that the MODIS/LST product can be a valuable additional source of real time forcing data for regional operational snow models, especially in remote mountainous areas with sparse telemetric data.

Analysis and Characterization of Probability Distribution and Small-Scale Spatial Variability of Rainfall in Singapore Using a Dense Gauge Network

AbstractHourly rainfall measurements from a network of 49 rain gauges on the tropical island of Singapore are analyzed to characterize variability of rainfall for temporal and spatial scales ranging from 1 to 24 h and from 1 to 45 km, respectively. First, the probability distributions of rain rates are characterized using the method ofLmoments. The analysis showed that the Pearson type-3 (PE3) distribution best fitted the rain rates for all time scales of concern. The parameters of the PE3 distribution are found to be related to the time scale through simple power laws. Second, the spatial structure of rainfall is characterized using spatial correlations. The decay of correlations with intergauge distance is parameterized using a powered-exponential function. In general, thee-folding correlation distance (distance at which the correlation drops to 1/e) varied from 10 km at hourly scales to 33 km at daily scales. The study also examined diurnal, seasonal, and anisotropic patterns in the spatial correlation structure of rainfall. The rainfall patterns are smoothest in December and January and are most variable in February, April, and October. Diurnal analysis of spatial correlations showed that the rainfall patterns are smoothest in the early hours between 0100 and 0600 local time and are most variable during the afternoon between 1500 and 1900 local time. The results also showed complex anisotropic patterns in spatial correlations, with considerable dependence of rainfall orientation on spatial scale and the time of the year.

Spatial and temporal resolution considerations in evaluating and utilizing radar quantitative precipitation estimates

In recent years, Weather Surveillance Radar-1988 Doppler base data have become available at substantially finer spatial resolution—i.e., 0.25 km  0.5o (0.25 km  1o for use in precipitation processing) versus the legacy 1 km  1o. Quantitative precipitation estimation (QPE) at this higher resolution may yield greater accuracy in the discretization of precipitation to delineated stream and river basins, in turn resulting in operational benefit in improved performance of hydrologic models and forecasting tools. To assess this potential, 1-h radar QPEs were determined from two experimental, S-band radar systems across several spatial resolutions—starting with base reflectivity data at or near the newly available, fine resolution, and recombining them up to, and including, the legacy resolution. Next, 1-h QPE was calculated at these various resolutions (based on the traditional Z–R relationship) and matched against 1-h rain gauge accumulations from dense gauge networks. After performing several steps of manual quality control, the dataset contained over 9500 warm-season gauge–radar pairs; these were assessed in various sub-groups and configurations (including stratification by precipitation intensity and range from the radar) and in simulated small stream basins. In various statistical analyses, however, the error differences between the fine- and legacy-resolutions were not statistically significant. Several possible causes for this supposed counterintuitive result were investigated, including (1) increased sampling error/noisiness in smaller versus larger sample bins, (2) sub-beam factors acting on falling hydrometeors, and (3) the matter that temporal sampling frequencies employed operationally were not increased commensurately with the increase in spatial sampling resolution.