The Use of Radar and Gauge Measurements to Estimate Areal Precipitation for Several Czech River Basins

Two methods estimating areal precipitation for selected river basins in the Czech Republic are compared. The methods use radar precipitation (the radar-derived precipitation estimate based on column maximum reflectivity) and data from 81 on-line rain gauges routinely provided by the Czech Hydrometeorological Institute. Data from a dense network of climatological rain gauges (the average inter-station distance is approximately 8 km), the measurements of which are not available in real time, are utilized for the verification. The mean areal precipitation, which is used as the ground truth, is obtained by the weighted interpolation of the dense rain gauge network. The accuracy of the methods is evaluated by the root-mean-square-error.The first, pixel-related method merges radar precipitation with rain gauge data to obtain adjusted pixel values. The adjusting procedure combines radar and gauge values in one variable that is interpolated into all radar pixels. The adjusted pixel precipitation is calculated from radar precipitation and from the value of the combined variable. The areal estimates are determined by adding the corresponding pixel values. The second method applies a linear regression model to describe the relationship between the areal precipitation (dependent variable) and its estimates, which are determined from (i) non-adjusted radar precipitation and (ii) on-line rain gauge measurements interpolated into pixels. Classical linear regression, ridge regression and robust regression models are tested.Both the methods decrease the average areal error in comparison with the reference method, which uses the on-line rain gauge data only. The decrease is about 10% and 15% for the pixel-related and regression methods, respectively. When the estimates of the pixel-related method are included as predictors into the regression method then the improvement of accuracy is almost 25%.     

Delineation of flood generating processes and their hydrological response

Knowledge about flood generating processes can be beneficial for numerous applications. Especially in the context of climate change impact assessment, daily patterns of meteorological and catchment state conditions leading to flood events (i.e., storylines) may be of value. Here, we propose an approach to identify storylines of flood generation using daily weather and snow cover observations. The approach is tested for and applied to a typical pre‐Alpine catchment in the period between 1961 and 2014. Five precipitation parameters were determined that describe temporal and spatial characteristics of the flood associated precipitation events. The catchment's snow coverage was derived using statistical relationships between a satellite‐derived snow cover climatology and station snow measurements. Moreover, (pre‐) event snow melt sums were estimated using a temperature‐index model. Based on the precipitation and catchment state parameters, 5 storylines were identified with a cluster analysis: These are (a) long duration, low intensity precipitation events with high precipitation depths, (b) long duration precipitation events with high precipitation depths and episodes of high intensities, (c) shorter duration events with high or (d) low precipitation intensity, respectively, and (e) rain‐on‐snow events. The event groups have distinct hydrological characteristics that can largely be explained by the storylines' respective properties. The long duration, high intensity storyline leads to the most adverse hydrological response, namely, a combination of high peak magnitudes, high volumes, and long durations of threshold exceedance. The results show that flood generating processes in mesoscale catchments can be distinguished on the basis of daily meteorological and catchment state parameters and that these process types can explain the hydrological flood properties in a qualitative way. Hydrological simulations of daily resolution can thus be analysed with respect to flood generating processes.     

Estimation of tropical cyclone parameters and wind fields from SAR images

The traditional method of Synthetic Aperture Radar(SAR)wind field retrieval is based on an empirical relation between the near surface winds and the normalized radar backscatter cross section to estimate wind speeds,where this relation is called the geophysical model function(GMF).However,the accuracy rapidly decreases due to the impact of rainfall on the measurement of SAR and the saturation of backscattered intensity under the condition of tropical cyclone.Because of no available instrument synchronously monitoring rain rate on the satellite platform of SAR,we have to derive the precipitation of the SAR observation time from non-simultaneous passive microwave observations of rain in combination with geostationary IR images,and then use the model of rain correction to remove the impact of rain on SAR wind field measurements.For the saturation of radar backscatter cross section in high wind speed conditions,we develop an approach to estimate tropical cyclone parameters and wind fields based on the improved Holland model and the SAR image features of tropical cyclone.To retrieve the low-to-moderate wind speed,the wind direction of tropical cyclone is estimated from the SAR image using wavelet analysis.And then the maximum wind speed and the central pressure of tropical cyclone are calculated by a least square minimization of the difference between the improved Holland model and the low-to-moderate wind speed retrieved from SAR.In addition,wind fields are estimated from the improved Holland model using the above-mentioned parameters of tropical cyclone as input.To evaluate the accuracy of our approach,the SAR images of typhoon Aere,typhoon Khanun,and hurricane Ophelia are used to estimate tropical cyclone parameters and wind fields,which are compared with the best track data and reanalyzed wind fields of the Joint Typhoon Warning Center(JTWC)and the Hurricane Research Division(HRD).The results indicate that the tropical cyclone center,maximum wind speed,and central pressure are generally consistent with the best track data,and wind fields agree well with reanalyzed data from HRD.     

On the computation of area probabilities based on a spatial stochastic model for precipitation cells and precipitation amounts

A main task of weather services is the issuing of warnings for potentially harmful weather events. Automated warning guidances can be derived, e.g., from statistical post-processing of numerical weather prediction using meteorological observations. These statistical methods commonly estimate the probability of an event (e.g. precipitation) occurring at a fixed location (a point probability). However, there are no operationally applicable techniques for estimating the probability of precipitation occurring anywhere in a geographical region (an area probability). We present an approach to the estimation of area probabilities for the occurrence of precipitation exceeding given thresholds. This approach is based on a spatial stochastic model for precipitation cells and precipitation amounts. The basic modeling component is a non-stationary germ-grain model with circular grains for the representation of precipitation cells. Then, we assign a randomly scaled response function to each precipitation cell and sum these functions up to obtain precipitation amounts. We derive formulas for expectations and variances of point precipitation amounts and use these formulas to compute further model characteristics based on available sequences of point probabilities. Area probabilities for arbitrary areas and thresholds can be estimated by repeated Monte Carlo simulation of the fitted precipitation model. Finally, we verify the proposed model by comparing the generated area probabilities with independent rain gauge adjusted radar data. The novelty of the presented approach is that, for the first time, a widely applicable estimation of area probabilities is possible, which is based solely on predicted point probabilities (i.e., neither precipitation observations nor further input of the forecaster are necessary). Therefore, this method can be applied for operational weather predictions.     

Validation of ERS scatterometer‐derived soil moisture data in the central part of the Duero Basin,Spain

The objective of this study was to validate the soil moisture data derived from coarse‐resolution active microwave data (50 km) from the ERS scatterometer. The retrieval technique is based on a change detection method coupled with a data‐based modelling approach to account for seasonal vegetation dynamics. The technique is able to derive information about the soil moisture content corresponding to the degree of saturation of the topmost soil layer (∼5 cm). To estimate profile soil moisture contents down to 100 cm depth from the scatterometer data, a simple two‐layer water balance model is used, which generates a red noise‐like soil moisture spectrum. The retrieval technique had been successfully applied in the Ukraine in a previous study. In this paper, the performance of the model in a semi‐arid Mediterranean environment characterized by low annual precipitation (400 mm), hot dry summers and sandy soils is investigated. To this end, field measurements from the REMEDHUS soil moisture station network in the semi‐arid parts of the Duero Basin (Spain) were used. The results reveal a significant coefficient of determination (R² = 0·75) for the averaged 0–100 cm soil moisture profile and a root mean square error (RMSE) of 2·2 vol%. The spatial arrangement of the REMEDHUS soil moisture stations also allowed us to study the influence of the small‐scale variability of soil moisture within the ERS scatterometer footprint. The results show that the small‐scale variability in the study area is modest and can be explained in terms of texture fraction distribution in the soil profiles. Copyright © 2004 John Wiley & Sons, Ltd.     

Observing System Simulation Experiment for Global Precipitation Mission

From the suite of future Global Precipitation Mission (GPM) satellites we have selected 11 of the possible contributors to the NASA’s International precipitation measurement program. The Observing System Simulation Experiments (OSSE) presented here explores the predictive usefulness of this suite of satellites. In order to carry out such experiments a Nature Run based on results from a state of the model is required. For that purpose we have selected recent past runs from the European Center for Medium Range Forecasts (ECMWF). These were designated as special data sets for OSSEs in partnership between NASA, NCEP/EMC, and NOAA. In order to test the usefulness of these future GPM-based precipitation measurements we first identify the typical orbits of eleven satellites. Along these orbital tracks we generate proxy precipitation data sets from the ECMWF Nature Run. This method of extraction of precipitation data set from a Nature Run is described in this paper. This methodology also requires a fraternal twin model (different from the Nature Run) in which the usefulness of the proposed GPM proxy data sets from the Nature Run are systematically evaluated in a forecast mode. The procedure for incorporation of the rainfall data sets is called the rain rate initialization. Data from one or more satellites are sequentially introduced into the fraternal twin model (which is the Florida State University Global Spectral Model) during the initialization phase for a number of experiments. After the initialization of such precipitation data sets, forecast experiments are carried out with the fraternal twin. The question asked is, as we introduce more and more GPM satellites how close do the forecasts from the fraternal twin approach the Nature Run? The results from this experimentation show that very promising improvements for short-range precipitation forecast skills are attainable from the proposed suite of GPM satellites.     

A multi‐source precipitation estimation approach to fill gaps over a radar precipitation field: a case study in the Colorado River Basin

The present work develops an approach to seamlessly blend satellite, available radar, climatological and gauge precipitation products to fill gaps in ground‐based radar precipitation field. To mix different precipitation products, the error of any of the products relative to each other should be removed. For bias correction, the study uses an ensemble‐based method that aims to estimate spatially varying multiplicative biases in SPEs using a radar precipitation product. A weighted successive correction method (SCM) is used to make the merging between error corrected satellite and radar precipitation estimates. In addition to SCM, we use a combination of SCM and Bayesian spatial model for merging the rain gauges (RGs) and climatological precipitation sources with radar and SPEs. We demonstrated the method using a satellite‐based hydro‐estimator; a radar‐based, stage‐II; a climatological product, Parameter‐elevation Regressions on Independent Slopes Model and a RG dataset for several rain events from 2006 to 2008 over an artificial gap in Oklahoma and a real radar gap in the Colorado River basin. Results show that: the SCM method in combination with the Bayesian spatial model produced a precipitation product in good agreement with independent measurements. The study implies that using the available radar pixels surrounding the gap area, RG, Parameter‐elevation Regressions on Independent Slopes Model and satellite products, a radar‐like product is achievable over radar gap areas that benefit the operational meteorology and hydrology community. Copyright © 2013 John Wiley & Sons, Ltd.     

Conditional simulation of remotely sensed rainfall data using a non-Gaussian v-transformed copula

Quantification of rainfall and its spatial and temporal variability is extremely important for reliable hydrological and meteorological modeling. While rain gauge measurements do not provide reasonable areal representation of rainfall, remotely sensed precipitation estimates offer much higher spatial resolution. However, uncertainties associated with remotely sensed rainfall estimates are not well quantified. This issue is important considering the fact that uncertainties in input rainfall are the main sources of error in hydrologic processes. Using an ensemble of rainfall estimates that resembles multiple realizations of possible true rainfall, one can assess uncertainties associated with remotely sensed rainfall data. In this paper, ensembles are generated by imposing rainfall error fields over remotely sensed rainfall estimates. A non-Gaussian copula-based model is introduced for simulation of rainfall error fields. The v-transformed copula is employed to describe the dependence structure of rainfall error estimates without the influence of the marginal distribution. Simulations using this model can be performed unconditionally or conditioned on ground reference measurements such that rain gauge data are honored at their locations. The presented model is implemented for simulation of rainfall ensembles across the Little Washita watershed, Oklahoma. The results indicate that the model generates rainfall fields with similar spatio-temporal characteristics and stochastic properties to those of observed rainfall data.     

Variability of the spatial structure of intense Mediterranean precipitation

Intense Mediterranean precipitation can generate devastating flash floods. A better understanding of the spatial structure of intense rainfall is critical to better identify catchments that will produce strong hydrological responses. We focus on two intense Mediterranean rain events of different types that occured in 2002. Radar and rain gauge measurements are combined to have a data set with a high spatial (1 × 1 km²) and temporal (5 min) resolution. Two thresholds are determined using the quantiles of the rain rate values, corresponding to the precipitating system at large and to the intense rain cells. A method based on indicator variograms associated with the thresholds is proposed in order to automatically quantify the spatial structure at each time step during the entire rain events. Therefore, its variability within intense rain events can be investigated. The spatial structure is found to be homogeneous over periods that can be related to the dynamics of the events. Moreover, a decreasing time resolution (i.e., increasing accumulation period) of the rain rate data will stretch the spatial structure because of the advection of rain cells by the wind. These quantitative characteristics of the spatial structure of intense Mediterranean rainfall will be useful to improve our understanding of the dynamics of flash floods.     

Fog interception by non‐vascular epiphytes in tropical montane cloud forests: dependencies on gauge type and meteorological conditions

Precipitation is the most fundamental input of water for terrestrial ecosystems. Most precipitation inputs are vertical, via rain, but can be horizontal, via wind‐driven rain and snow, or, in some ecosystems such as tropical montane cloud forests (TMCFs), via fog interception. Fog interception can be particularly important in ecosystems where fog is frequently present and there are seasonal periods of lower rainfall. Epiphytes in trees are a major ecological component of TMCFs and are particularly dependent on fog interception during periods of lower rainfall because they lack access to soil water. But assessing fog interception by epiphytes remains problematic because: (i) a variety of field or laboratory methods have been used, yet comparisons of interception by epiphytes versus interception by various types of fog gauge are lacking; (ii) previous studies have not accounted for potential interactions between meteorological factors. We compared fog interception by epiphytes with two kinds of commonly used fog gauges and developed relations between fog interception and meteorological variables by conducting laboratory experiments that manipulated key fog characteristics and from field measurements of fog interception by epiphytes. Fog interception measured on epiphytes was correlated with that measured from fog gauges but was more than an order of magnitude smaller than the actual measurements from fog gauges, highlighting a key measurement issue. Our laboratory measurements spanned a broad range of liquid water content (LWC) values for fog and indicate how fog interception is sensitive to an interaction between wind speed and LWC. Based on our results, considered in concert with those from other studies, we hypothesize that fog interception is constrained when LWC is low or high, and that fog interception increases with wind speed for intermediate values of LWC—a net result of deposition, impaction, and evaporation processes—until interception begins to decrease with further increases in wind speed. Copyright © 2007 John Wiley & Sons, Ltd.     

Cover crops and precipitation influence soluble reactive phosphorus losses via tile drain discharge in an agricultural watershed

Subsurface tile drainage speeds water removal from agricultural fields that are historically prone to flooding. While managed drainage systems improve crop yields, they can also contribute tothe eutrophication of downstream ecosystems, as tile-drained systems are conduits for nutrients to adjacent waterways. The changing climate of the Midwestern US has already altered precipitation regimes which will likely continue into the future, with unknown effects on tile drain water and nutrient loss to waterways. Adding vegetative cover (i.e., as winter cover crops) is one approach that can retain water and nutrients on fields to minimize export via tile drains. In the current study, we evaluate the effect of cover crops on tile drain discharge and soluble reactive phosphorus (SRP) loads using bi-monthly measurements from 43 unique tile outlets draining fields with or without cover crops in two watersheds in northern Indiana. Using four water years of data (n = 844 measurements), we examined the role of short-term antecedent precipitation conditions and variation in soil biogeochemistry in mediating the effect of cover crops on tile drain flow and SRP loads. We observed significant effects of cover crops on both tile drain discharge and SRP loads, but these results were season and watershed specific. Cover crop effects were identified only in spring, where their presence reduced tile drain discharge in both watersheds and SRP loads in one watershed. Varying effects on SRP loads between watersheds were attributed to different soil biogeochemical characteristics, where soils with lower bioavailable P and higher P sorption capacity were less likely to have a cover crop effect. Antecedent precipitation was important in spring, and cover crop differences were still evident during periods of wet and dry antecedent precipitation conditions. Overall, we show that cover crops have the potential to significantly decrease spring tile drain P export, and these effects are resilient to a wide range of precipitation conditions.     

Development of a rain‐on‐snow detection algorithm using passive microwave radiometry

Currently observed climate warming in the Arctic has numerous consequences. Of particular relevance, the precipitation regime is modified where mixed and liquid precipitation can occur during the winter season leading to rain‐on‐snow (ROS) events. This phenomenon is responsible for ice crust formation, which has a significant impact on ecosystems (such as biological, hydrological, ecological and physical processes). The spatially and temporally sporadic nature of ROS events makes the phenomenon difficult to monitor using meteorological observations. This paper focuses on the detection of ROS events using passive microwave (PMW) data from a modified brightness temperature (T_B) gradient approach at 19 and 37 GHz. The approach presented here was developed empirically for observed ROS events with coincident ground‐based PMW measurements in Sherbrooke, Quebec, Canada. It was then tested in Nunavik, Quebec, with the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E). We obtained a detection accuracy of 57, 71 and 89% for ROS detection for three AMSR‐E grid cells with a maximum error of 7% when considering all omissions and commissions with regard to the total number of AMSR‐E passes throughout the winter period. Copyright © 2016 John Wiley & Sons, Ltd.     

Implications of Warm Rain in Shallow Cumulus and Congestus Clouds for Large-Scale Circulations

Space-borne observations reveal that 20–40% of marine convective clouds below the freezing level produce rain. In this paper we speculate what the prevalence of warm rain might imply for convection and large-scale circulations over tropical oceans. We present results using a two-column radiative–convective model of hydrostatic, nonlinear flow on a non-rotating sphere, with parameterized convection and radiation, and review ongoing efforts in high-resolution modeling and observations of warm rain. The model experiments investigate the response of convection and circulation to sea surface temperature (SST) gradients between the columns and to changes in a parameter that controls the conversion of cloud condensate to rain. Convection over the cold ocean collapses to a shallow mode with tops near 850 hPa, but a congestus mode with tops near 600 hPa can develop at small SST differences when warm rain formation is more efficient. Here, interactive radiation and the response of the circulation are crucial: along with congestus a deeper moist layer develops, which leads to less low-level radiative cooling, a smaller buoyancy gradient between the columns, and therefore a weaker circulation and less subsidence over the cold ocean. The congestus mode is accompanied with more surface precipitation in the subsiding column and less surface precipitation in the deep convecting column. For the shallow mode over colder oceans, circulations also weaken with more efficient warm rain formation, but only marginally. Here, more warm rain reduces convective tops and the boundary layer depth—similar to Large-Eddy Simulation (LES) studies—which reduces the integrated buoyancy gradient. Elucidating the impact of warm rain can benefit from large-domain high-resolution simulations and observations. Parameterizations of warm rain may be constrained through collocated cloud and rain profiling from ground, and concurrent changes in convection and rain in subsiding and convecting branches of circulations may be revealed from a collocation of space-borne sensors, including the Global Precipitation Measurement (GPM) and upcoming Aeolus missions.     

利用全极化微波辐射计资料反演台风境内海面风场

作为一种新兴的被动遥感技术,全极化微波辐射计不仅可以提供海面风速产品,还可以提供海面风向产品.以往利用全极化微波辐射计观测亮温进行海面风场反演仅在晴空条件下进行,本文通过对观测亮温结合台风区域海面风场的分布特征进行分析,验证了全极化微波辐射计具有在台风等恶劣天气条件下进行海面风场观测的能力.基于敏感性分析实验,确定使用6.8 GHz和10.7 GHz等低频通道组合可进行台风区域内海面风场反演.其中,海面风速反演使用基于统计的多元线性回归算法,同时对海面温度、大气水汽含量、云中液态水含量及降水强度等物理量进行反演计算,为海面风向反演做准备.海面风向反演使用物理统计法进行,借鉴散射计风向反演使用的最大似然估计法.通过在全极化辐射传输前向模型中加入降水对大气透过率的影响、设计第三和第四Stokes通道亮温环境影响修正函数,在实现台风区域内海面风向反演的同时减小了反演误差.通过对“云娜”台风境内海面风场进行数值计算,验证了本文反演算法的可行性,并对反演误差的空间分布特征进行了分析.将2004年各台风过程的海面风场反演结果与散射计风场产品进行对比,海面风速和海面风向反演的均方根误差分别为1.64 m·s^-1和18.02°.     

Numerical investigation of the impact of uncertainties in satellite rainfall estimation and land surface model parameters on simulation of soil moisture

This study aims at evaluating the uncertainty in the prediction of soil moisture (1D, vertical column) from an offline land surface model (LSM) forced by hydro-meteorological and radiation data. We focus on two types of uncertainty: an input error due to satellite rainfall retrieval uncertainty, and, LSM soil-parametric error. The study is facilitated by in situ and remotely sensed data-driven (precipitation, radiation, soil moisture) simulation experiments comprising a LSM and stochastic models for error characterization. The parametric uncertainty is represented by the generalized likelihood uncertainty estimation (GLUE) technique, which models the parameter non-uniqueness against direct observations. Half-hourly infra-red (IR) sensor retrievals were used as satellite rainfall estimates. The IR rain retrieval uncertainty is characterized on the basis of a satellite rainfall error model (SREM). The combined uncertainty (i.e., SREM + GLUE) is compared with the partial assessment of uncertainty. It is found that precipitation (IR) error alone may explain moderate to low proportion of the soil moisture simulation uncertainty, depending on the level of model accuracy—50–60% for high model accuracy, and 20–30% for low model accuracy. Comparisons on the basis of two different sites also yielded an increase (50–100%) in soil moisture prediction uncertainty for the more vegetated site. This study exemplified the need for detailed investigations of the rainfall retrieval-modeling parameter error interaction within a comprehensive space-time stochastic framework for achieving optimal integration of satellite rain retrievals in land data assimilation systems.     

Implications for groundwater recharge from stable isotopic composition of precipitation in Hawai'i during the 2017–2018 La Niña

The project captured a subset of the hydrological cycle for the tropical island of O'ahu, linking precipitation to groundwater recharge and aquifer storage. We determined seasonal storm events contributed more to aquifer recharge than year-round baseline orographic trade wind rainfall. Hydrogen and oxygen isotope values from an island-wide rain collector network with 20 locations deployed for 16 months and sampled at 3-month intervals were used to create the first local meteoric water line for O'ahu. Isotopic measurements were influenced by the amount effect, seasonality, storm type, and La Niña, though little elevation control was noted. Certain groundwater compositions from legacy data showed a strong similarity with collected precipitation from our stations. The majority of these significant relationships were between wet season precipitation and groundwater. A high number of moderate and heavy rainfall days during the dry season, large percentage of event-based rainfall, and wind directions outside of the typical NE trade wind direction were characteristics of the 2017–2018 wet season. This indicates that the majority of wet season precipitation is from event-based storms rather than typical trade wind weather. The deuterium-excess values provided the strongest evidence of a relationship between groundwater and different precipitation sources, indicating that this may be a useful metric for determining the extent of recharge from different rain events and systems.     

Retrieval of Large-Scale Hydrological Signals in Africa from GRACE Time-Variable Gravity Fields

Since its launch in April 2002, the Gravity Recovery and Climate Experiment (GRACE) mission is recording the Earth’s time-variable gravity field with temporal and spatial resolutions of typically 7–30?days and a few hundreds of kilometers, allowing the monitoring of continental water storage variations from both continental and river-basin scales. We investigate here large scale hydrological variations in Africa using different GRACE spherical harmonic solutions, using different processing strategies (constrained and unconstrained solutions). We compare our GRACE estimates to different global hydrology models, with different land-surface schemes and also precipitation forcing. We validate GRACE observations through two different techniques: first by studying desert areas, providing an estimate of the precision. Then we compare GRACE recovered mass variations of main lakes to volume changes derived from radar altimetry measurements. We also study the differences between different publicly available precipitation datasets from both space measurements and ground rain gauges, and their impact on soil-moisture estimates.     

Estimation of rainfall from infrared‐microwave satellite data for basin‐scale hydrologic modelling

The infrared‐microwave rainfall algorithm (IMRA) was developed for retrieving spatial rainfall from infrared (IR) brightness temperatures (TBs) of satellite sensors to provide supplementary information to the rainfall field, and to decrease the traditional dependency on limited rain gauge data that are point measurements. In IMRA, a SLOPE technique (ST) was developed for discriminating rain/no‐rain pixels through IR image cloud‐top temperature gradient, and 243K as the IR threshold temperature for minimum detectable rainfall rate. IMRA also allows for the adjustment of rainfall derived from IR‐TB using microwave (MW) TBs. In this study, IMRA rainfall estimates were assessed on hourly and daily basis for different spatial scales (4, 12, 20, and 100 km) using NCEP stage IV gauge‐adjusted radar rainfall data, and daily rain gauge data. IMRA was assessed in terms of the accuracy of the rainfall estimates and the basin streamflow simulated by the hydrologic model, Sacramento soil moisture accounting (SAC‐SMA), driven by the rainfall data. The results show that the ST option of IMRA gave accurate satellite rainfall estimates for both light and heavy rainfall systems while the Hessian technique only gave accurate estimates for the convective systems. At daily time step, there was no improvement in IR‐satellite rainfall estimates adjusted with MW TBs. The basin‐scale streamflow simulated by SAC‐SMA driven by satellite rainfall data was marginally better than when SAC‐SMA was driven by rain gauge data, and was similar to the case using radar data, reflecting the potential applications of satellite rainfall in basin‐scale hydrologic modelling. Copyright © 2010 John Wiley & Sons, Ltd.     

On the influence of microphysics parametrization on the rainfall rates in numerical models of clouds

Accuracy of formulas for growth by accretion and evaporation of rain in bulk parametrization of these processes for the case of light and moderate precipitation is investigated. It is done by comparison of results from two simple models: with bulk approach and with exact calculations of growth or evaporation of drops in each size bin separately. Growth by accretion is accurately represented in bulk parametrization but rain evaporation is overpredicted. Corrected formula for rain evaporation is suggested.     

Comparison of radar and gauge precipitation data in watershed models across varying spatial and temporal scales

Precipitation is a key control on watershed hydrologic modelling output, with errors in rainfall propagating through subsequent stages of water quantity and quality analysis. Most watershed models incorporate precipitation data from rain gauges; higher‐resolution data sources are available, but they are associated with greater computational requirements and expertise. Here, we investigate whether the Multisensor Precipitation Estimator (MPE or Stage IV Next‐Generation Radar) data improve the accuracy of streamflow simulations using the Soil and Water Assessment Tool (SWAT), compared with rain gauge data. Simulated flows from 2002 to 2010 at five timesteps were compared with observed flows for four nested subwatersheds of the Neuse River basin in North Carolina (21‐, 203‐, 2979‐, and 10 100‐km² watershed area), using a multi‐objective function, informal likelihood‐weighted calibration approach. Across watersheds and timesteps, total gauge precipitation was greater than radar precipitation, but radar data showed a conditional bias of higher rainfall estimates during large events (>25–50 mm/day). Model parameterization differed between calibrations with the two datasets, despite the fact that all watershed characteristics were the same across simulation scenarios. This underscores the importance of linking calibration parameters to realistic processes. SWAT simulations with both datasets underestimated median and low flows, whereas radar‐based simulations were more accurate than gauge‐based simulations for high flows. At coarser timesteps, differences were less pronounced. Our results suggest that modelling efforts in watersheds with poor rain gauge coverage can be improved with MPE radar data, especially at short timesteps. Published 2013. This article is a U.S. Government work and is in the public domain in the USA.