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The daily precipitation data generated by dynamical models, including regional climate models, generally suffer from biases in distribution and spatial dependence. These are serious flaws if the data are intended to be applied to hydrometeorological studies. This paper proposes a scheme for correcting the biases in both aspects simultaneously. The proposed scheme consists of two steps: an aggregation step and a disaggregation step. The first one aims to obtain a smoothed precipitation pattern that must be retained in correcting the bias, and the second aims to make up for the deficient spatial variation of the smoothed pattern. In both steps, the Gaussian copula plays important roles since it not only provides a feasible way to correct the spatial correlation of model simulations but also can be extended for large-dimension cases by imposing a covariance function on its correlation structure. The proposed scheme is applied to the daily precipitation data generated by a regional climate model. We can verify that the biases are satisfactorily corrected by examining several statistics of the corrected data. [ABSTRACT FROM AUTHOR]

This paper estimates rainfall totals at 17 Greenland meteorological stations, subjecting data from in situ precipitation gauge measurements to seven different precipitation phase schemes to separate rainfall and snowfall amounts. To correct the resulting snow/rain fractions for undercatch, we subsequently use a dynamic correction model (DCM) for automatic weather stations (AWS, Pluvio gauges) and a regression analysis correction method for staffed stations (Hellmann gauges). With observations ranging from 5% to 57% for cumulative totals, rainfall accounts for a considerable fraction of total annual precipitation over Greenland’s coastal regions, with the highest rain fraction in the south (Narsarsuaq). Monthly precipitation and rainfall totals are used to evaluate the regional climate model RACMO2.3. The model realistically captures monthly rainfall and total precipitation (R = 0.3–0.9), with generally higher correlations for rainfall for which the undercatch correction factors (1.02–1.40) are smaller than those for snowfall (1.27–2.80), and hence the observations are more robust. With a horizontal resolution of 5.5 km and simulation period from 1958 to the present, RACMO2.3 therefore is a useful tool to study spatial and temporal variability of rainfall in Greenland, although further statistical downscaling may be required to resolve the steep rainfall gradients. [ABSTRACT FROM AUTHOR]

In this paper, the sensitivity of the Weather Research and Forecasting (WRF) Model to the nudging parameters in simulating July--August (JJA) precipitation was assessed with 16 experiments over the Coordinated Regional Climate Downscaling Experiment East Asia II (CORDEX-EA-II) domain. The effects of various nudging parameters in spectral nudging (referred to as SN) and grid nudging (referred to as AN) experiments are examined, including wavenumbers, relaxation time, nudging levels, and nudging variables for SN and relaxation time and nudging variables for AN. Results showed that the applications of spectral nudging and grid nudging methods in WRF simulations can improve the model's ability to reproduce the JJA extreme precipitation event and accompanying large-scale fields in 2003. The major findings include 1) spectral nudging is superior to grid nudging in simulating heavy rainfall and low-level circulation, 2) nudging both kinematic and thermodynamic variables is efficient to better simulate the JJA precipitation for both SN and AN simulations, 3) in SNsimulations, the options of wavenumbers display stronger impact on JJA precipitation if nudging solely the kinematic variables instead of both kinematic and thermodynamic variables over wet subregions, and 4) the free developed large-scale processes associated with small nudging wavenumbers can diminish the improvement from nudging both kinematic and thermodynamic variables in simulating subseasonal variations of precipitation. Overall, the experiment that adopts spectral nudging of both kinematic and thermodynamic variables, 1-h relaxation time, and four or eight nudging wavenumbers captures the characteristics of summer climate more reasonably. [ABSTRACT FROM AUTHOR]

This paper describes the assumptions, equations, and procedures of the RheaG weather generator algorithm (WGA). RheaG was conceived for the generation of robust daily meteorological time series, whether in static or transient climate conditions. Here we analyze its performance in four Iberian locations—Bilbao, Barcelona, Madrid, and Sevilla—with differentiated climate characteristics. To validate the RheaG WGA, we compared observed and generated meteorological time series' statistical properties of precipitation, maximum temperature, and minimum temperature for all four locations. We also compared observed and simulated rain events spell length probabilities in all four locations. Finally, RheaG includes two weather generation procedures: one in which monthly mean values for meteorological variables are unconstrained and one in which they are constrained according to a predefined baseline climate variability. Here, we compare the two weather generation procedures included in RheaG using the observed data from Barcelona. Our results present a high agreement in the statistical properties and the rain spell length probabilities between observed and generated meteorological time series. Our results show that RheaG accurately reproduces seasonal patterns of the observed meteorological time series for all four locations, and it is even able to differentiate two climatic seasons in Bilbao that are also present in the observed data. We find a trade-off between generation procedures in which the unconstrained procedure better reproduces the variability of monthly and yearly precipitation than the constrained one, but the constrained procedure is able to keep the same climatic signal across meteorological time series. Thus, the first procedure is more accurate, but the latter is able to maintain spatial autocorrelation among generated meteorological time series. [ABSTRACT FROM AUTHOR]

This research is Part II of a two-part study that evaluates the ability of image-processing and select machine-learning algorithms to detect, classify, and track midlatitude mesoscale convective systems (MCSs) in radar-reflectivity images for the conterminous United States. This paper focuses on the tracking portion of this framework. Tracking is completed through a two-step process using slice (snapshots of instantaneous MCS intensity) data generated in Part I. The first step is to perform spatiotemporal matching, which associates slices through temporally adjacent radar-reflectivity images to generate swaths, or storm tracks. When multiple slices are found to be matches, a difference-minimization procedure is used to associate the most similar slice with the existing swath. Once this step is completed, a second step combines swaths that are spatiotemporally close. Tracking performance is assessed by calculating select metrics for all available swath-building perturbations to determine the optimal approach in tracking. Frequency maps and time series generated from the swaths suggest that the spatiotemporal occurrence of these swaths is reasonable as determined from previous work. Further, these events exhibit a diurnal cycle that is distinct from that of overall convection for the conterminous United States. Last, machine-learning predictions are found to limit areas of high MCS frequency to the central and eastern Great Plains. [ABSTRACT FROM AUTHOR]

This research evaluates the ability of image-processing and select machine-learning algorithms to identify midlatitude mesoscale convective systems (MCSs) in radar-reflectivity images for the conterminous United States. The process used in this study is composed of two parts: segmentation and classification. Segmentation is performed by identifying contiguous or semicontiguous regions of deep, moist convection that are organized on a horizontal scale of at least 100 km. The second part, classification, is performed by first compiling a database of thousands of precipitation clusters and then subjectively assigning each sample one of the following labels: 1) midlatitude MCS, 2) unorganized convective cluster, 3) tropical system, 4) synoptic system, or 5) ground clutter and/or noise. The attributes of each sample, along with their assigned label, are used to train three machine-learning algorithms: random forest, gradient boosting, and ''XGBoost.'' Results using a testing dataset suggest that the algorithms can distinguish between MCS and non-MCS samples with a high probability of detection and low probability of false detection. Further, the trained algorithm predictions are well calibrated, allowing reliable probabilistic classification. The utility of this two-step procedure is illustrated by generating spatial frequency maps of automatically identified precipitation clusters that are stratified by using various reflectivity and probabilistic prediction thresholds. These results suggest that machine learning can add value by limiting the amount of false-positive (non-MCS) samples that are not removed by segmentation alone. [ABSTRACT FROM AUTHOR]

This paper looks at the analysis of U.K. monthly rainfall data from a long-term persistence viewpoint. Different modeling approaches are considered, taking into account the strong dependence and the seasonality in the data. The results indicate that the most appropriate model is the one that presents cyclical long-run dependence with the order of integration being positive though small, and the cycles having a periodicity of about a year. [ABSTRACT FROM AUTHOR]

The Snowy Precipitation Enhancement Research Project (SPERP) was undertaken in winters from May 2005 to June 2009 in the Snowy Mountains region of southeastern Australia. Part I of this paper describes the design and implementation of the project, as well as the characteristics of the key datasets collected during the field phase. The primary analysis in this paper (Part II) shows an unequivocal impact on the targeting of seeding material, with the maximum level of silver in snow samples collected from the primary target area found to be significantly greater in seeded than unseeded experimental units (EUs). A positive but not statistically significant impact on precipitation was found. Further analysis shows that a substantial source of uncertainty in the estimation of the impacts of seeding on precipitation is associated with EUs where the seeding generators operated for relatively few hours. When the analysis is repeated using only EUs with more than 45 generator hours, the increase in precipitation in the primary target area is 14%% at the 8%% significance level. When applying that analysis to the overall target area, the precipitation increase is 14%% at the 3%% significance level. A secondary analysis of the ratio of silver to indium in snow supports the hypothesis that seeding material affected the cloud microphysics. Other secondary analyses reveal that seeding had an impact on virtually all of the physical variables examined in a manner consistent with the seeding hypothesis. [ABSTRACT FROM AUTHOR]

In common with much of the western United States, the Pacific Northwest (defined in this paper as Washington and Oregon) has experienced an unusual number of droughts in the past decade. This paper describes three of these droughts in terms of the precipitation, temperature, and soil moisture anomalies, and discusses different drought impacts experienced in the Pacific Northwest (PNW). For the first drought, in 2001, low winter precipitation in the PNW produced very low streamflow that primarily affected farmers and hydropower generation. For the second, in 2003, low summer precipitation in Washington (WA), and low summer precipitation and a warm winter in Oregon (OR) primarily affected streamflow and forests. For the last, in 2005, a lack of snowpack due to warm temperatures during significant winter precipitation events in WA, and low winter precipitation in OR, had a variety of different agricultural and hydrologic impacts. Although the proximal causes of droughts are easily quantified, the ultimate causes are not as clear. Better precipitation observations in the PNW are required to provide timely monitoring of conditions leading to droughts to improve prediction in the future. [ABSTRACT FROM AUTHOR]

The historical climate record and seasonal temperature and precipitation records provide useful datasets for making short-term drought predictions. A variety of methods have exploited these resources, but few have quantitatively measured uncertainties associated with predictions of drought index values commonly used in management plans. In this paper, stochastic approaches for estimating uncertainty are applied to drought index predictions. National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) seasonal forecasts and resampling of nearest-neighbor residuals are incorporated to measure uncertainty in monthly forecasts of Palmer drought severity index (PDSI) and standardized precipitation index (SPI) in central South Carolina. Kuiper skill scores of PDSI indicate good forecast performance with up to 3-month lead time and improvements for 1-month-lead SPI forecasts. NOAA CPC climate outlook improved the forecast skill by as much as 40%, and the degree of improvement varies by season and forecast lead time. [ABSTRACT FROM AUTHOR]

A generalized method is presented to derive a “two scale” raindrop size distribution (DSD) model over a spatial or temporal domain in which a statistical rain parameter relation exists. The two-scale model is generally defined as a model in which one DSD parameter is allowed to vary rapidly and the other is constant over a certain space or time domain. The existence of a rain parameter relation such as the radar reflectivity–rainfall rate (Z–R) relation over a spatial or temporal domain is an example of such a two-scale DSD model. A procedure is described that employs a statistical rain parameter relation with an assumption of the gamma DSD model. An example using Z–R relations obtained at Kototabang, West Sumatra, is presented. The result shows that the resulting two-scale DSD model expressed by conventional DSD parameters depends on the assumed value of parameter μ while rain parameter relations such as k–Ze relations from those models using different μ values are very close to each other, indicating the stability of the model against the variation of μ and the validity of this method. The result is applied to the DSD model for the Tropical Rainfall Measuring Mission (TRMM) precipitation radar 2A25 (versions 5 and 6) algorithm. The derivation procedure of the 2A25 DSD model is described. Through the application of this model, it has become possible to make a logically well-organized rain profiling algorithm and reasonable rain attenuation correction and rainfall estimates, as described in an earlier paper by Iguchi et al. [ABSTRACT FROM AUTHOR]

This paper reports on the development of a new statistical tool that generates probabilistic outlooks of seasonal precipitation anomaly categories over Africa. Called the seasonal performance probability (SPP), it quantitatively evaluates the probability of precipitation to finish at predefined percent-of-normal anomaly categories corresponding to below-average (<80% of normal), average (80%-120% of normal), and above-average (>120% of normal) conditions. This is accomplished by applying methods for kernel density estimation (KDE), which compute smoothed, continuous density functions on the basis of more than 30 years of historical precipitation data from the Africa Rainfall Climatology, version 2, dataset (ARC2) for the remaining duration of a monsoon season. Discussion of various parameterizations of KDE and testing to determine optimality of density estimates (and thus performance of SPP for operational monitoring) are presented. Verification results from 2006 to 2015 show that SPP reliably provides probabilistic outcomes of seasonal rainfall anomaly categories by after the early to midstages of rain seasons for the major monsoon regions in East Africa, West Africa, and southern Africa. SPP has proven to be a useful tool by enhancing operational climate monitoring at CPC for its prognostic capability for famine early warning scenarios over Africa. These insights are anticipated to translate into better decision-making in food security, planning, and response objectives for the U.S. Agency for International Development/Famine Early Warning Systems Network (USAID/FEWS NET). [ABSTRACT FROM AUTHOR]

Recent studies have used numerical models to estimate the collection efficiency of solid precipitation gauges when exposed to the wind in both shielded and unshielded configurations. The models used computational fluid dynamics (CFD) simulations of the airflow pattern generated by the aerodynamic response to the gauge-shield geometry. These are used as initial conditions to perform Lagrangian tracking of solid precipitation particles. Validation of the results against field observations yielded similarities in the overall behavior, but the model output only approximately reproduced the dependence of the experimental collection efficiency on wind speed. This paper presents an improved snowflake trajectory modeling scheme due to the inclusion of a dynamically determined drag coefficient. The drag coefficient was estimated using the local Reynolds number as derived from CFD simulations within a time-independent Reynolds-averaged Navier-Stokes approach. The proposed dynamic model greatly improves the consistency of results with the field observations recently obtained at the Marshall Field winter precipitation test bed in Boulder, Colorado. [ABSTRACT FROM AUTHOR]

Long-term global gridded datasets of observed precipitation are essential for the analysis of the global water and energy cycle, its variability, and possible changes. Several institutions provide those datasets. In 2005 the Global Precipitation Climatology Centre (GPCC) published the so-called Variability Analysis of Surface Climate Observations (VASClimO) dataset. This dataset is especially designed for the investigation of temporal change and variability. To date, however, the GPCC has not published how this dataset has been produced. This paper aims to fill this gap. It provides detailed information on how stations are selected and how data are quality controlled and interpolated. The dataset is based only on station records covering at least 90% of the period 1951-2000. The time series of 9343 stations were used. However, these stations are distributed very inhomogeneously around the globe; 4094 of these stations are within Germany and France. The VASClimO dataset is interpolated from relative deviations of observed monthly precipitation, leading to considerably lower interpolation errors than direct interpolation or the interpolation of absolute deviations. The retransformation from interpolated relative deviations to precipitation is done with local long-term averages of precipitation interpolated from data of the Food and Agriculture Organization of the United Nations. The VASClimO dataset has been interpolated with a method that is based on local station correlations (LSC) that is introduced here. It is compared with ordinary kriging and three versions of Shepard's method. LSC outperforms these methods, especially with respect to the spatial maxima of interpolation errors. [ABSTRACT FROM AUTHOR]

This paper describes an improved edition of the climate division dataset for the conterminous United States (i.e., version 2). The first improvement is to the input data, which now include additional station networks, quality assurance reviews, and temperature bias adjustments. The second improvement is to the suite of climatic elements, which now includes both maximum and minimum temperatures. The third improvement is to the computational approach, which now employs climatologically aided interpolation to address topographic and network variability. Version 2 exhibits substantial differences from version 1 over the period 1895-2012. For example, divisional averages in version 2 tend to be cooler and wetter, particularly in mountainous areas of the western United States. Division-level trends in temperature and precipitation display greater spatial consistency in version 2. National-scale temperature trends in version 2 are comparable to those in the U.S. Historical Climatology Network whereas version 1 exhibits less warming as a result of historical changes in observing practices. Divisional errors in version 2 are likely less than 0.5°C for temperature and 20 mm for precipitation at the start of the record, falling rapidly thereafter. Overall, these results indicate that version 2 can supersede version 1 in both operational climate monitoring and applied climatic research. [ABSTRACT FROM AUTHOR]

This paper describes and evaluates a satellite rainfall estimation technique that combines infrared and lightning information to estimate precipitation in deep convective systems. The algorithm is developed and tested using seven years (2002-08) of TRMM measurements over the southern United States during the warm season. Lightning information is coupled with a modified IR-based convective-stratiform technique (CST) and produces a lightning-enhanced CST (CSTL). Both the CST and CSTL are then applied to the training (2002-04) and independent (2005-08) datasets. In general, this study shows significant improvement over the IR rainfall estimates (rain area, intensity, and volume) by adding lightning information. The CST and CSTL display critical skill in estimating warm-season precipitation and the performance is very stable. The CST can generally identify the heavy (convective) and light rain regions, while CSTL further identifies convective areas that are missed by CST and removes convective cores that are incorrectly defined by CST. Specifically, the CSTL improves the convective cell detection by 5% and reduces the convective false alarm rate by more than 30%. Similarly, CSTL substantially improves the CST in the overall estimate of instantaneous rainfall rates. For example, when compared with passive microwave estimates, CSTL increases the correlation coefficient by 30%, reduces the bias by 50%, and reduces RMSE by 25%. Both CST and CSTL reproduce the rain area and volume fairly accurately over a region, although both techniques show some degree of overestimation relative to radar estimates. [ABSTRACT FROM AUTHOR]

The 1981-2010 'U.S. Climate Normals' released by the National Oceanic and Atmospheric Administration's (NOAA) National Climatic Data Center include a suite of monthly, seasonal, and annual statistics that are based on precipitation, snowfall, and snow-depth measurements. This paper describes the procedures used to calculate the average totals, frequencies of occurrence, and percentiles that constitute these normals. All parameters were calculated from a single, state-of-the-art dataset of daily observations, taking care to produce normals that were as representative as possible of the full 1981-2010 period, even when the underlying data records were incomplete. In the resulting product, average precipitation totals are available at approximately 9300 stations across the United States and parts of the Caribbean Sea and Pacific Ocean islands. Snowfall and snow-depth statistics are provided for approximately 5300 of those stations, as compared with several hundred stations in the 1971-2000 normals. The 1981-2010 statistics exhibit the familiar climatological patterns across the contiguous United States. When compared with the same calculations for 1971-2000, the later period is characterized by a smaller number of days with snow on the ground and less total annual snowfall across much of the contiguous United States; wetter conditions over much of the Great Plains, Midwest, and northern California; and drier conditions over much of the Southeast and Pacific Northwest. These differences are a reflection of the removal of the 1970s and the addition of the 2000s to the 30-yr-normals period as part of this latest revision of the normals. [ABSTRACT FROM AUTHOR]

In the author's experience as a forecaster, commercial aircraft sometimes report turbulence beneath midlevel clouds that extend above upper frontal zones. Turbulence caused by Kelvin-Helmholtz instability occurs in upper frontal zones with strong vertical shear of horizontal winds. However, the turbulence seems to occur not only in the cloud bases (where upper frontal zones are) but also below the cloud bases where the vertical shear is not strong. Because those clouds are usually accompanied by precipitation that does not reach the ground, cooling by evaporation or sublimation seems to contribute to the generation of turbulence. In this paper, the mechanisms generating turbulence below midlevel cloud bases are examined by using observations and high-resolution three-dimensional numerical simulations with idealized initial conditions. The numerical simulations showed that the following sequence of events led to turbulence. Falling snow sublimated below cloud bases and cooled the air, which created absolute instability. This generated Rayleigh-Bénard convection cells. The vertical motion caused turbulence. The horizontal scale of the convection was about 800-1000 m, and the variations of vertical wind velocity were up to about 7 m s−1. The cloud base was accompanied by a virga-like distribution of snow. Sensitivity experiments showed the appropriate conditions to cause the turbulence: 1) the cloud-base temperature was between about 0° and −15°C, 2) the relative humidity in subcloud layers was sufficiently low, and 3) the stability in subcloud layers was weak. The results of the numerical simulations agreed well with the observations. [ABSTRACT FROM AUTHOR]

This paper describes a new gridded, daily 29-yr precipitation estimation dataset centered over Africa at 0.1° spatial resolution. Called the African Rainfall Climatology, version 2 (ARC2), it is a revision of the first version of the ARC. Consistent with the operational Rainfall Estimation, version 2, algorithm (RFE2), ARC2 uses inputs from two sources: 1) 3-hourly geostationary infrared (IR) data centered over Africa from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) and 2) quality-controlled Global Telecommunication System (GTS) gauge observations reporting 24-h rainfall accumulations over Africa. The main difference with ARC1 resides in the recalibration of all Meteosat First Generation (MFG) IR data (1983-2005). Results show that ARC2 is a major improvement over ARC1. It is consistent with other long-term datasets, such as the Global Precipitation Climatology Project (GPCP) and Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP), with correlation coefficients of 0.86 over a 27-yr period. However, a marginal summer dry bias that occurs over West and East Africa is examined. Daily validation with independent gauge data shows RMSEs of 11.3, 13.4, and 14, respectively, for ARC2, Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis 3B42, version 6 (3B42v6), and the CPC morphing technique (CMORPH) for the West African summer season. The ARC2 RMSE is slightly higher for Ethiopia than those of CMORPH and 3B42v6. Both daily and monthly validations suggested that ARC2 underestimations may be attributed to the unavailability of daily GTS gauge reports in real time, and deficiencies in the satellite estimate associated with precipitation processes over coastal and orographic areas. However, ARC2 is expected to provide users with real-time monitoring of the daily evolution of precipitation, which is instrumental in improved decision making in famine early warning systems. [ABSTRACT FROM AUTHOR]

Coastal ocean ecosystems are impacted by atmospheric conditions and events, including episodic severe systems such as hurricanes as well as more regular seasonal events. The complexity of the atmosphere-ocean relationship makes establishing concrete connections difficult. In this paper, this relationship is assessed through synoptic climatological methods, a technique well established in applied climatological research but heretofore rarely used in assessing coastal ocean water quality and ecological status. Historical sea level pressure data are used to define 10 circulation patterns across the southeastern United States and adjacent Gulf of Mexico, based on the spatial pattern of sea level pressure, which can then be associated with the presence of cyclones, precipitation, and wind stress. The frequency of these patterns, and their deviation from climatological means, is then compared with Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) chlorophyll observations over the Florida Bay and south Florida shelf for the period 1997-2010. Several circulation patterns indicative of cyclonic activity over the broader region are associated with increased chlorophyll levels in the study area, while several other patterns, indicative of anticyclonic conditions, are associated with decreased chlorophyll levels. These relationships are spatially and temporally variable, generally with stronger correlations observed in winter and spring, and farther north in the study region when compared with more southern locations near the Florida Keys. The results here demonstrate the potential of using synoptic analysis and derived statistics for tracking and modeling changes in chlorophyll and other indicators related to water quality and biological health. [ABSTRACT FROM AUTHOR]

The NOAA National Climatic Data Center maintains tables for temperature and precipitation extremes in each of the U.S. states. Many of these tables were several years out of date, however, and therefore did not include a number of recent record-setting meteorological observations. Furthermore, there was no formal process for ensuring the currency of the tables or evaluating observations that might tie or break a statewide climate record. This paper describes the evaluation and revision of the statewide climate-extremes tables for all-time maximum and minimum temperature, greatest 24-h precipitation and snowfall, and greatest snow depth (the five basic climate elements observed on a daily basis by the NOAA Cooperative Weather Network). The process resulted in the revision of 40% of the values listed in those tables and underscored both the necessity of manual quality-assurance methods and the importance of continued climate-monitoring and data-rescue activities to ensure that potential record values are not overlooked. [ABSTRACT FROM AUTHOR]

Short-term automated forecasting (nowcasting) of precipitation has traditionally been done using radar reflectivity data; recent research, however, indicates that using specific differential phase Kdp has several advantages over using reflectivity for estimating rainfall. This paper presents an evaluation of the characteristics of nowcasting Kdp-based rainfall fields using the Collaborative Adaptive Sensing of the Atmosphere Kdp estimation and nowcasting methods applied to approximately 42 h of X-band radar network data. The results show that Kdp-based rainfall fields exhibit lifetimes of ~17 min as compared with ~15 min for rainfall fields derived from reflectivity Zh in a continuous (cross correlation based) sense. Categorical (skill score based) lifetimes of ~26 min were observed for Kdp-based rainfall fields as compared with ~30 min for Zh-based rainfall fields. Radar-rain gauge verification showed that Kdp-based rainfall estimates consistently outperformed Zh-based estimates out to a lead time of 30 min, but the difference between the two estimators decreased in terms of normalized standard error with increasing lead time. [ABSTRACT FROM AUTHOR]

The short-term predictability of precipitation patterns observed by meteorological radar is an important concept as it establishes a means to characterize precipitation and provides an upper limit on the extent of useful nowcasting. Predictability also varies on the basis of spatial and temporal scales of the observed meteorological phenomena. This paper describes an investigation of the short-term predictability of precipitation patterns containing microalpha (0.2-2 km) to mesobeta (20-200 km) scales using high-resolution (0.5 km-1 min-1 dB Z) composite radar reflectivity data, extending the analysis presented in previous work to smaller space and time scales. An experimental approach is used in which continuous and categorical lifetimes of radar reflectivity fields in Eulerian and Lagrangian space are used to quantify short-term predictability. The space-time scale dependency of short-term predictability is analyzed, and a practical upper limit on the extent of Lagrangian persistence-based nowcasting is estimated. Connections to the predictability of larger scales are made within the context of previous work. The results show that short-term predictability estimates in terms of lifetime are approximately 14-15 and 20-21 min in Eulerian and Lagrangian space, respectively, and suggest that a linear relationship exists between predictability and space-time structure from microalpha to macrobeta (2000-10 000 km) scales. [ABSTRACT FROM AUTHOR]

Studies carried out worldwide show that topography influences rainfall climatology. As in most western Mediterranean regions, the mountainous Cévennes-Vivarais area in France regularly experiences extreme precipitation that may lead to devastating flash floods. Global warming could further aggravate this situation, but this possibility cannot be confirmed without first improving the understanding of the role of topography in the regional climate and, in particular, for extreme rainfall events. This paper focuses on organized banded rainfall and evaluates its contribution to the rainfall climatology of this region. Stationary rainfall systems made up of such bands are triggered and enhanced by small-scale interactions between the atmospheric flow and the relief. Rainbands are associated with shallow convection and are also present in deep-convection events for specific flux directions. Such precipitation patterns are difficult to observe both with operational weather radar networks, which are not designed to observe low-level convection within complex terrain, and with rain gauge networks, for which gauge spacing is typically larger than the bandwidth. A weather class of banded orographic shallow-convection events is identified, and the contribution of such events to annual or seasonal precipitation over the region is assessed. Moreover, a method is also proposed to quantify the contribution of banded convection during specific deep-convection events. It is shown that even though these orographically driven banded precipitation events produce moderate precipitation intensities they have long durations and therefore represent a significant amount of the rainfall climatology of the region, producing up to 40%% of long-term total precipitation at certain locations. [ABSTRACT FROM AUTHOR]

Conventional methods of viewing and combining retrieved geophysical fields from polar-orbiting satellites often complicate the work of end users because of the erratic time differences between overpasses, the significant time gaps between elements of a composite image, or simply the different requirements for interpretation between contributing instruments. However, it is possible to mitigate these issues for any number of retrieved quantities in which the tracer lifetime exceeds the sampling time. This paper presents a method that uses ''advective blending'' to create high-fidelity composites of data from polar-orbiting satellites at high temporal resolution, including a characterization of error as a function of time gap between satellite overpasses. The method is especially effective for tracers with lifetimes of longer than 7 h. Examples are presented using microwave-based retrievals of total precipitable water (TPW) over the ocean, from the Cooperative Institute for Meteorological Satellite Studies (CIMSS) Morphed Integrated Microwave Imagery at CIMSS TPW product (MIMIC-TPW). The mean average error of a global 0.25°° ×× 0.25°° product at 1-h resolution is 0.5--2 mm, which is very reasonable for most applications. [ABSTRACT FROM AUTHOR]

In northwestern North America, which is a large area with complex physiography, Climatic Research Unit (CRU) Time Series, version 2.1, (TS 2.1) gridded monthly mean 2-m temperatures are systematically lower than interpolated monthly averaged North American Regional Reanalysis (NARR) pressure-level temperatures----in particular, in the winter. Quantification of these differences based on CRU gridded observations can be used to estimate pressure-level temperatures from CRU 2-m temperatures (1901--2002) that predate the NARR period (since 1979). Such twentieth-century pressure-level temperature fields can be used in glacier mass-balance modeling and as an alternative to calibrating general circulation model control runs, avoiding the need for accurate boundary layer parameterization. In this paper, an approach is presented that is transferable to moisture, wind, and other 3D fields with potential applications in wind power generation, ecology, and air quality. At each CRU grid point, the difference between CRU and NARR is regressed against seven predictors in CRU (mean temperature, daily temperature range, precipitation, vapor pressure, cloud cover, and number of wet and frost days) for the period of overlap between CRU and NARR (1979--2002). Bayesian model averaging (BMA) is used to avoid overfitting the CRU--NARR differences and underestimating uncertainties. In cross validations, BMA provides reliable posterior predictions of the CRU--NARR differences and outperforms predictions from three alternative models: the constant model (24-yr mean), the regression model of highest Bayesian model probability, and the full model retaining all seven predictors in CRU. [ABSTRACT FROM AUTHOR]

Today, latent heat flux and precipitation over the global ocean surface can be determined from microwave satellite data as a basis for estimating the related fields of the ocean surface freshwater flux. The Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data (HOAPS) is the only generally available satellite-based dataset with consistently derived global fields of both evaporation and precipitation and hence of freshwater flux for the period 1987--2005. This paper presents a comparison of the evaporation E, precipitation P, and the resulting freshwater flux E −− P in HOAPS with recently available reference datasets from reanalysis and other satellite observation projects as well as in situ ship measurements. In addition, the humidity and wind speed input parameters for the evaporation are examined to identify sources for differences between the datasets. Results show that the general climatological patterns are reproduced by all datasets. Global mean time series often agree within about 10%% of the individual products, while locally larger deviations may be found for all parameters. HOAPS often agrees better with the other satellite-derived datasets than with the in situ or the reanalysis data. The agreement usually improves in regions of good in situ sampling statistics. The biggest deviations of the evaporation parameter result from differences in the near-surface humidity estimates. The precipitation datasets exhibit large differences in highly variable regimes with the largest absolute differences in the ITCZ and the largest relative biases in the extratropical storm-track regions. The resulting freshwater flux estimates exhibit distinct differences in terms of global averages as well as regional biases. In comparison with long-term mean global river runoff data, the ocean surface freshwater balance is not closed by any of the compared fields. The datasets exhibit a positive bias in E −− P of 0.2--0.5 mm day−−1, which is on the order of 10%% of the evaporation and precipitation estimates. [ABSTRACT FROM AUTHOR]

In this paper, wintertime precipitation from a variety of observational datasets, regional climate models (RCMs), and general circulation models (GCMs) is averaged over the state of California and compared. Several averaging methodologies are considered and all are found to give similar values when the model grid spacing is less than 3°. This suggests that California is a reasonable size for regional intercomparisons using modern GCMs. Results show that reanalysis-forced RCMs tend to significantly overpredict California precipitation. This appears to be due mainly to the overprediction of extreme events; RCM precipitation frequency is generally underpredicted. Overprediction is also reflected in wintertime precipitation variability, which tends to be too high for RCMs on both daily and interannual scales. Wintertime precipitation in most (but not all) GCMs is underestimated. This is in contrast to previous studies based on global blended gauge-satellite observations, which are shown here to underestimate precipitation relative to higher-resolution gauge-only datasets. Several GCMs provide reasonable daily precipitation distributions, a trait that does not seem to be tied to model resolution. The GCM daily and interannual variabilities are generally underpredicted. [ABSTRACT FROM AUTHOR]

Nonuniform beam filling associated with the vertical variation of atmospheric reflectivity is an important source of error in the estimation of rainfall rates by radar. It is, however, possible to correct for this error if the vertical profile of reflectivity (VPR) is known. This paper presents a method for identifying VPRs from volumetric radar data. The method aims at improving an existing algorithm based on the analysis of ratios of radar measurements at multiple elevation angles. By adding a rainfall classification procedure defining more homogeneous precipitation patterns, the issue of VPR homogeneity is specifically addressed. The method is assessed using the dataset from a volume-scanning strategy for radar quantitative precipitation estimation designed in 2002 for the Bollène radar (France). The identified VPR is more representative of the rain field than are other estimated VPRs. It has also a positive impact on radar data processing for precipitation estimation: while scatter remains unchanged, an overall bias reduction at all time steps is noticed (up to 6% for all events) whereas performance varies with type of events considered (mesoscale convective systems, cold fronts, or shallow convection) according to the radar-observation conditions. This is attributed to the better processing of spatial variations of the vertical profile of reflectivity for the stratiform regions. However, adaptation of the VPR identification in the difficult radar measurement context in mountainous areas and to the rainfall classification procedure proved challenging because of data fluctuations. [ABSTRACT FROM AUTHOR]

In this work, the authors present observations of enhanced temporal coherency beyond that expected using the observations of the standard deviation of the Doppler velocities and the assumption of a family of exponentially decaying autocorrelation functions. The purpose of this paper is to interpret these observations by developing the complex amplitude autocorrelation function when both incoherent and coherent backscatter are present. Using this expression, it is then shown that when coherent scatter is present, the temporal coherency increases as observed. Data are analyzed in snow and in rain. The results agree with the theoretical expectations, and the authors interpret this agreement as an indication that coherent scatter is the likely explanation for the observed enhanced temporal coherency. This finding does not affect decorrelation times measured using time series. However, when the time series is not available (as in theoretical studies), the times to decorrelation are often computed based upon the assumptions that the autocorrelation function is a member of the family of exponentially decaying autocorrelation functions and that the signal decorrelation is due solely to the Doppler velocity fluctuations associated with incoherent scatter. Such an approach, at times, may significantly underestimate the true required times to decorrelation thus leading to overestimates of statistical reliability of parameters. [ABSTRACT FROM AUTHOR]

This paper describes a comprehensive set of fully automated quality assurance (QA) procedures for observations of daily surface temperature, precipitation, snowfall, and snow depth. The QA procedures are being applied operationally to the Global Historical Climatology Network (GHCN)-Daily dataset. Since these data are used for analyzing and monitoring variations in extremes, the QA system is designed to detect as many errors as possible while maintaining a low probability of falsely identifying true meteorological events as erroneous. The system consists of 19 carefully evaluated tests that detect duplicate data, climatological outliers, and various inconsistencies (internal, temporal, and spatial). Manual review of random samples of the values flagged as errors is used to set the threshold for each procedure such that its false-positive rate, or fraction of valid values identified as errors, is minimized. In addition, the tests are arranged in a deliberate sequence in which the performance of the later checks is enhanced by the error detection capabilities of the earlier tests. Based on an assessment of each individual check and a final evaluation for each element, the system identifies 3.6 million (0.24%) of the more than 1.5 billion maximum/minimum temperature, precipitation, snowfall, and snow depth values in GHCN-Daily as errors, has a false-positive rate of 1%−2%, and is effective at detecting both the grossest errors as well as more subtle inconsistencies among elements. [ABSTRACT FROM AUTHOR]

This paper uses observations from Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and microwave imager (TMI) to evaluate the cloud microphysical schemes in the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5; version 3.7.4) for a wintertime frontal precipitation system over the eastern Pacific Ocean. By incorporating a forward radiative transfer model, the radar reflectivity and brightness temperatures are simulated and compared with the observations at PR and TMI frequencies. The main purpose of this study is to identify key differences among the five schemes [including Simple ice, Reisner1, Reisner2, Schultz, and Goddard Space Flight Center (GSFC) microphysics scheme] in the MM5 that may lead to significant departures of simulated precipitation properties from both active (PR) and passive (TMI) microwave observations. Radiative properties, including radar reflectivity, attenuation, and scattering in precipitation liquid and ice layers are investigated. In the rain layer, most schemes are capable of reproducing the observed radiative properties to a reasonable degree; the Reisner2 simulation, however, produces weaker reflectivity and stronger attenuation than the observations, which is possibly attributable to the larger intercept parameter ( N0r) applied in this run. In the precipitation ice layer, strong evidence regarding the differences in the microphysical and radiative properties between a narrow cold-frontal rainband (NCFR) and a wide cold-frontal rainband (WCFR) within this frontal precipitation system is found. The performances of these schemes vary significantly on simulating the microphysical and radiative properties of the frontal rainband. The GSFC scheme shows the least bias, while the Reisner1 scheme has the largest bias in the reflectivity comparison. It appears more challenging for the model to replicate the scattering signatures obtained by the passive sensor (TMI). Despite the common problem of excessive scattering in the WCFR (stratiform precipitation) region in every simulation, the magnitude of the scattering maximum seems better represented in the Reisner2 scheme. The different types of precipitation ice, snow, and graupel are found to behave differently in the relationship of scattering versus reflectivity. The determinative role of the precipitation ice particle size distribution (intercept parameters) is extensively discussed through sensitivity tests and a single-layer radiative transfer model. [ABSTRACT FROM AUTHOR]

Four independently developed high-resolution precipitation products [HRPPs; the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), the Climate Prediction Center Morphing Method (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and the National Research Laboratory (NRL) blended precipitation dataset (NRL-blended)], with a spatial resolution of 0.25° and a temporal resolution of 3 h, were compared with surface rain measurements for the four summer seasons (June, July, and August) from 2003 to 2006. Surface measurements are 1-min rain gauge data from the Automated Weather Station (AWS) network operated by the Korean Meteorological Administration (KMA) over South Korea, which consists of about 520 sites. The summer mean rainfall and diurnal cycles of TMPA are comparable to those of the AWS, but with larger magnitudes. The closer agreement of TMPA with surface observations is due to the adjustment of the real-time version of TMPA products to monthly gauge measurements. However, the adjustment seems to result in significant overestimates for light or moderate rain events and thus increased RMS error. In the other three products (CMORPH, PERSIANN, and NRL-blended), significant underestimates are evident in the summer mean distribution and in scatterplots for the grid-by-grid comparison. The magnitudes of the diurnal cycles of the three products appear to be much smaller than those suggested by AWS, although CMORPH shows nearly the same diurnal phase as in AWS. Such underestimates by three methods are likely due to the deficiency of the passive microwave (PMW)-based rainfall retrievals over the South Korean region. More accurate PMW measurements (in particular by the improved land algorithm) seem to be a prerequisite for better estimates of the rain rate by HRPP algorithms. This paper further demonstrates the capability of the Korean AWS network data for validating satellite-based rain products. [ABSTRACT FROM AUTHOR]

Drought is a complex phenomenon that is difficult to accurately describe because its definition is both spatially variant and context dependent. Decision makers in local, state, and federal agencies commonly use operational drought definitions that are based on specific drought index thresholds to trigger water conservation measures and determine levels of drought assistance. Unfortunately, many state drought plans utilize operational drought definitions that are derived subjectively and therefore may not be appropriate for triggering drought responses. This paper presents an objective methodology for establishing operational drought definitions. The advantages of this methodology are demonstrated by calculating meteorological drought thresholds for the Palmer drought severity index, the standardized precipitation index, and percent of normal precipitation using both station and climate division data from Texas. Results indicate that using subjectively derived operational drought definitions may lead to over- or underestimating true drought severity. Therefore, it is more appropriate to use an objective location-specific method for defining operational drought thresholds. [ABSTRACT FROM AUTHOR]

There have been debates and differences of opinion over the validity of using drop size distribution (DSD) models to characterize precipitation microphysics and to retrieve DSD parameters from multiparameter radar measurements. In this paper, simulated and observed rain DSDs are used to evaluate moment estimators. Seven estimators for gamma DSD parameters are evaluated in terms of the biases and fractional errors of five integral parameters: radar reflectivity (ZH), differential reflectivity (ZDR), rainfall rate (R), mean volume diameter (Dm), and total number concentration (NT). It is shown that middle-moment estimators such as M234 (using the second-third-fourth moments) produce smaller errors than lower- and higher-moment estimators if the DSD follows the gamma distribution. However, if there are model errors, the performance of M234 degrades. Even though the DSD parameters can be biased in moment estimators, integral parameters are usually not. Maximum likelihood (ML) and L-moment (LM) estimators perform similarly to low-moment estimators such as M012. They are sensitive to both model error and the measurement errors of the low ends of DSDs. The overall differences among M234, M246, and M346 are not substantial for the five evaluated parameters. This study also shows that the discrepancy between the radar and disdrometer observations cannot be reduced by using these estimators. In addition, the previously found constrained-gamma model is shown not to be exclusively determined by error effects. Rather, it is equivalent to the mean function of normalized DSDs derived through Testud's approach, and linked to precipitation microphysics. [ABSTRACT FROM AUTHOR]

The evaluation strategies outlined in this paper constitute a set of tools beneficial to the development and documentation of robust automated quality assurance (QA) procedures. Traditionally, thresholds for the QA of climate data have been based on target flag rates or statistical confidence limits. However, these approaches do not necessarily quantify a procedure’s effectiveness at detecting true errors in the data. Rather, as illustrated by way of an “extremes check” for daily precipitation totals, information on the performance of a QA test is best obtained through a systematic manual inspection of samples of flagged values combined with a careful analysis of geographical and seasonal patterns of flagged observations. Such an evaluation process not only helps to document the effectiveness of each individual test, but, when applied repeatedly throughout the development process, it also aids in choosing the optimal combination of QA procedures and associated thresholds. In addition, the approach described here constitutes a mechanism for reassessing system performance whenever revisions are made following initial development. [ABSTRACT FROM AUTHOR]

This paper presents and evaluates a method for the construction of long-range and wide-area temporal spatial datasets of daily precipitation and temperature (maximum and minimum). This method combines the interpolation of daily ratios/fractions derived from ground-based meteorological station records and respective fields of monthly estimates. Data sources for the described implementation over the conterminous United States (CONUS) are two independent and quality-controlled inputs: 1) an enhanced compilation of daily observations derived from the National Climatic Data Center digital archives and 2) the Parameter–Elevation Regressions on Independent Slopes Model (PRISM) maps. The results of this study show that this nonconventional interpolation preserves the spatial and temporal distribution of both the PRISM maps (monthly, topography-sensitive patterns) and the original daily observations. Statistics of a preliminary point comparison with the observed values at high-quality and independent reference sites show a reasonable agreement and a noticeable improvement over the nearest station method in orographically sensitive areas. The implemented datasets provide daily precipitation and temperature values at 2.5-min (around 4 km) resolution for 1960–2001. Combining seamless spatial and temporal coverage and topographic sensitivity characteristics, the datasets offer the potential for supporting current and future regional and historical hydrologic assessments over the CONUS. [ABSTRACT FROM AUTHOR]

The spectral latent heating (SLH) algorithm was developed to estimate apparent heat source (Q1) profiles for the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) in Parts I and II of this study. In this paper, the SLH algorithm is used to estimate apparent moisture sink (Q2) profiles. The procedure of Q2 retrieval is the same as that of heating retrieval except for using the Q2 profile lookup tables derived from numerical simulations of tropical cloud systems from the Tropical Ocean and Global Atmosphere (TOGA) Coupled Ocean–Atmosphere Response Experiment (COARE) utilizing a cloud-resolving model (CRM). The Q2 profiles were reconstructed from CRM-simulated parameters with the COARE table and then compared with CRM-simulated “true” Q2 profiles, which were computed directly from the water vapor equation in the model. The consistency check indicates that discrepancies between the SLH-reconstructed and CRM-simulated profiles for Q2, especially at low levels, are larger than those for Q1 and are attributable to moistening for the nonprecipitating region that SLH cannot reconstruct. Nevertheless, the SLH-reconstructed total Q2 profiles are in good agreement with the CRM-simulated ones. The SLH algorithm was applied to PR data, and the results were compared with Q2 profiles derived from the budget study. Although discrepancies between the SLH-retrieved and sounding-based profiles for Q2 for the South China Sea Monsoon Experiment (SCSMEX) are larger than those for heating, key features of the vertical profiles agree well. The SLH algorithm can also estimate differences of Q2 between the western Pacific Ocean and the Atlantic Ocean, consistent with the results from the budget study. [ABSTRACT FROM AUTHOR]

High-quality, daily meteorological data at high spatial resolution are essential for a variety of hydrologic and ecological modeling applications that support environmental risk assessments and decision making. This paper describes the development, application, and assessment of methods to construct daily high-resolution (∼50-m cell size) meteorological grids for the 2003 calendar year in the Upper South Santiam Watershed (USSW), a 500-km2 mountainous catchment draining the western slope of the Oregon Cascade Mountains. Elevations within the USSW ranged from 194 to 1650 m. Meteorological elements modeled were minimum and maximum temperature; total precipitation, rainfall, and snowfall; and solar radiation and radiation-adjusted maximum temperature. The Parameter–Elevation Regressions on Independent Slopes Model (PRISM) was used to interpolate minimum and maximum temperature and precipitation. The separation of precipitation into rainfall and snowfall components used a temperature-based regression function. Solar radiation was simulated with the Image-Processing Workbench. Radiation-based adjustments to maximum temperature employed equations developed from data in the nearby H. J. Andrews Experimental Forest. The restrictive terrain of the USSW promoted cold-air drainage and temperature inversions by reducing large-scale airflow. Inversions were prominent nearly all year for minimum temperature and were noticeable even for maximum temperature during the autumn and winter. Precipitation generally increased with elevation over the USSW. In 2003, precipitation was nearly always in the form of rain at the lowest elevations but was about 50% snow at the highest elevations. Solar radiation followed a complex pattern related to terrain slope, aspect, and position relative to other terrain features. Clear, sunny days with a large proportion of direct radiation exhibited the greatest contrast in radiation totals, whereas cloudy days with primarily diffuse radiation showed little contrast. Radiation-adjusted maximum temperatures showed similar patterns. The lack of a high-quality observed dataset was a major issue in the interpolation of precipitation and solar radiation. However, observed data available for the USSW were superior to those available for most mountainous regions in the western United States. In this sense, the methods and results presented here can inform others performing similar studies in other mountainous regions. [ABSTRACT FROM AUTHOR]

Polarization radar offers the promise of much more accurate rainfall-rate R estimates than are possible from radar reflectivity factor Z alone, not only by better characterization of the drop size distribution, but also by more reliable correction for attenuation and the identification of hail. However, practical attempts to implement retrieval algorithms have been hampered by the difficulty in coping with the inherent noise in the polarization parameters. In this paper, a variational retrieval scheme is described that overcomes these problems by employing a forward model for differential reflectivity Zdr and differential phase shift φdp and iteratively refining the coefficient a in the relationship Z = aRb such that the difference between the forward model and the measurements is minimized in a least squares sense. Two methods are used to ensure that a varies smoothly in both range and azimuth. In range, a is represented by a set of cubic-spline basis functions; in azimuth, the retrieval at one ray is used as a constraint on the next. The result of this smoothing is that the retrieval is tolerant of random errors in Zdr of up to 1 dB and in φdp of up to 5°. Correction for attenuation is achieved simply and effectively by including its effects in the forward model. If hail is present then the forward model is unable to match the observations of Zdr and φdp simultaneously. This enables a first pass of the retrieval to be used to identify the radar pixels that contain hail, followed by a second pass in which the fraction of the Z in those gates that is due to hail is retrieved, this time with the scheme being able to forward-model both Zdr and φdp accurately. The scheme is tested on S-band radar data from southern England in cases of rain, spherical hail, oblate hail, and mixtures of rain and hail. It is found to be robust and stable, even in the presence of differential phase shift on backscatter. [ABSTRACT FROM AUTHOR]

The spectral latent heating (SLH) algorithm was developed for the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) in Part I of this study. The method uses PR information [precipitation-top height (PTH), precipitation rates at the surface and melting level, and rain type] to select heating profiles from lookup tables. Heating-profile lookup tables for the three rain types—convective, shallow stratiform, and anvil rain (deep stratiform with a melting level)—were derived from numerical simulations of tropical cloud systems from the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) utilizing a cloud-resolving model (CRM). To assess its global application to TRMM PR data, the universality of the lookup tables from the TOGA COARE simulations is examined in this paper. Heating profiles are reconstructed from CRM-simulated parameters (i.e., PTH, precipitation rates at the surface and melting level, and rain type) and are compared with the true CRM-simulated heating profiles, which are computed directly by the model thermodynamic equation. CRM-simulated data from the Global Atmospheric Research Program Atlantic Tropical Experiment (GATE), South China Sea Monsoon Experiment (SCSMEX), and Kwajalein Experiment (KWAJEX) are used as a consistency check. The consistency check reveals discrepancies between the SLH-reconstructed and Goddard Cumulus Ensemble (GCE)-simulated heating above the melting level in the convective region and at the melting level in the stratiform region that are attributable to the TOGA COARE table. Discrepancies in the convective region are due to differences in the vertical distribution of deep convective heating due to the relative importance of liquid and ice water processes, which varies from case to case. Discrepancies in the stratiform region are due to differences in the level separating upper-level heating and lower-level cooling. Based on these results, improvements were made to the SLH algorithm. Convective heating retrieval is now separated into upper-level heating due to ice processes and lower-level heating due to liquid water processes. In the stratiform region, the heating profile is shifted up or down by matching the melting level in the TOGA COARE lookup table with the observed one. Consistency checks indicate the revised SLH algorithm performs much better for both the convective and stratiform components than does the original one. The revised SLH algorithm was applied to PR data, and the results were compared with heating profiles derived diagnostically from SCSMEX sounding data. Key features of the vertical profiles agree well—in particular, the level of maximum heating. The revised SLH algorithm was also applied to PR data for February 1998 and February 1999. The results are compared with heating profiles derived by the convective–stratiform heating (CSH) algorithm. Because observed information on precipitation depth is used in addition to precipitation type and intensity, differences between shallow and deep convection are more distinct in the SLH algorithm in comparison with the CSH algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a new, purely physical approach to simulate ice-particle scattering at microwave frequencies. Temperature-dependent ice particle size distributions measured by aircraft in midlatitude frontal systems are used to represent the distribution of precipitation-sized frozen hydrometeors above the freezing level through derived radar reflectivity–snow water content (Z–M) relationships. The discrete dipole approximation is employed to calculate optical properties of selected types of idealized nonspherical ice particles (hexagonal columns, four-arm rosettes, and six-arm rosettes). Based on those assumptions, passive microwave optical properties are calculated using radar observations from Gotland Island in the Baltic Sea. These forward-simulated brightness temperatures are compared with observed data from both the Advanced Microwave Scanning Radiometer (AMSR-E) and the Advanced Microwave Sounding Unit-B (AMSU-B). Results show that the new ice scattering/microphysics model is able to generate brightness temperatures that are consistent with AMSR and AMSU-B observations of two light-winter-precipitation cases. The overall differences among the various ice-habit results at 89 GHz are generally not that expansive, whereas the AMSU-B 150-GHz comparisons show increased sensitivity to ice-particle shapes. [ABSTRACT FROM AUTHOR]

A retrieval algorithm is described to estimate vertical profiles of precipitation ice water content and liquid water content in tropical cyclones and convection over ocean from combined spaceborne radar and radiometer measurements. In the algorithm, the intercept parameter N0s in the exponential particle size distribution for rain, snow, and graupel are adjusted iteratively to minimize the difference between observed brightness temperatures and simulated ones by using a simulated annealing optimization method. Sensitivity tests are performed to understand the effects of the input parameters. The retrieval technique is investigated using the Earth Resources (ER)-2 aircraft Doppler radar and Advanced Microwave Precipitation Radiometer data in tropical cyclones and convection. An indirect validation is performed by comparing the measured and retrieved 50-GHz (independent channel) brightness temperature. The global agreement shows not only the quality of the inversion procedure, but also the consistency of the retrieved parameters with observations. The direct validation of the ice water content retrieval by using the aircraft in situ microphysical measurements indicates that the algorithm can provide reliable ice water content estimates, especially in stratiform regions. In convective regions, the large variability of the microphysical characteristics causes a large uncertainty in the retrieval, although the mean difference between the retrieved ice water content and aircraft-derived ice water content is very small. The ice water content estimated by a radar-only empirical relationship is higher than those retrieved by the combined algorithm and derived by the aircraft in situ observations. The new combined algorithm contains information that should improve ice water content estimates from either radar-only or passive microwave–only measurements. An important caveat for this study is that it concerns precipitation estimates. In this paper, ice and liquid water content should be interpreted as precipitation ice and liquid water content. [ABSTRACT FROM AUTHOR]

Intercomparisons of satellite rainfall products have historically focused on the issue of global mean biases. Regional and temporal variations in these biases, however, are equally important for many climate applications. This has led to a critical examination of rainfall estimates from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR). Because of the time-dependent nature of these biases, it is not possible to apply corrections based on regionally defined characteristics. Instead, this paper seeks to relate PR–TMI differences to physical variables that can lead to a better understanding of the mechanisms responsible for the observed differences. To simplify the analysis, issues related to differences in rainfall detection and intensity are investigated separately. For clouds identified as raining by both sensors, differences in rainfall intensity are found to be highly correlated with column water vapor. Adjusting either TMI or PR rain rates based on this simple relationship, which is relatively invariant over both seasonal and interannual time scales, results in a 65%–75% reduction in the rms difference between seasonally averaged climate rainfall estimates. Differences in rainfall detection are most prominent along the midlatitude storm tracks, where widespread, isolated convection trailing frontal systems is often detected only by the higher-resolution PR. Conversely, over the East China Sea clouds below the ∼18-dBZ PR rainfall detection threshold are frequently identified as raining by the TMI. Calculations based on in situ aerosol data collected south of Japan support a hypothesis that high concentrations of sulfate aerosols may contribute to abnormally high liquid water contents within nonprecipitating clouds in this region. [ABSTRACT FROM AUTHOR]

The spatial distribution of precipitation occurrence has important implications for numerous applications ranging from defining cloud radiative effects to modeling hydrologic runoff, statistical downscaling, and stochastic weather generation. This paper introduces a new method of describing the spatial characteristics of rainfall and snowfall that takes advantage of the high sensitivity and high resolution of the W-band cloud precipitation radar aboard CloudSat. The resolution dependence of precipitation occurrence is described by a two-parameter exponential function defined by a shape factor that governs the variation in the distances between precipitation events and a scale length that represents the overall probability of precipitation and number density of distinct events. Geographic variations in the shape factor and scale length are consistent with large-scale circulation patterns and correlate with environmental conditions on local scales. For example, a large contrast in scale lengths between land and ocean areas reflects the more extensive, widespread nature of precipitation over land than over ocean. An analysis of warm rain in the southeast Pacific reveals a shift from frequent isolated systems to less frequent but more regularly spaced systems along a transect connecting stratocumulus and trade cumulus cloud regimes. A similar analysis during the Amazon wet season reveals a relationship between the size and frequency of convection and zonal wind direction with precipitation exhibiting a more oceanic character during periods of westerly winds. These select examples demonstrate the utility of this approach for capturing the sensitivity of the spatial characteristics of precipitation to environmental influences on both local and larger scales. [ABSTRACT FROM AUTHOR]

In this study, a nonstationary standardized precipitation index (NSPI) is calculated by fitting precipitation data to the nonstationary gamma model with climate indices as covariates and compared with stationary standardized precipitation index (SSPI) in fitting observed precipitation and identifying meteorological droughts during 1964-2016 in the Yangtze River basin (YRB). Results show that changing trends of NSPI are roughly consistent with those of SSPI, but the NSPI performs better than SSPI for fitting precipitation. Using the NSPI, spatiotemporal variations and joint return period of drought characteristics are investigated by using modified Mann-Kendall and copula function. It is found that the west YRB experiences mostly severe droughts but shows a mitigating tendency, while in the north-central region, moderate droughts are dominant but have an aggravating tendency. Drought peak shares a spatial distribution pattern similar to intensity, with higher averages in the west and south-central basin and lower averages in the north-central part, but they exhibit a higher occurrence frequency of droughts. The joint return periods of drought characteristics reveal that slight and moderate droughts with duration D of no more than 3 months are more likely to occur in the YRB with return periods of 2-25 years. The severe- and extreme-intensity droughts with more than 3-month duration occur rarely in the YRB, but extreme peak droughts with slight or moderate intensity could hit most of the basin in 100 years. In addition, for most drought scenarios, droughts exhibit longer return periods in the north-central and southeast parts relative to other regions of the YRB. [ABSTRACT FROM AUTHOR]

A new dual-frequency (Ku and Ka band) nadir-pointing Doppler radar on the high-altitude NASA ER-2 aircraft, called the High-Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP), has collected data over severe thunderstorms in Oklahoma and Kansas during the Midlatitude Continental Convective Clouds Experiment (MC3E). The overarching motivation for this study is to understand the behavior of the dual-wavelength airborne radar measurements in a global variety of thunderstorms and how these may relate to future spaceborne-radar measurements. HIWRAP is operated at frequencies that are similar to those of the precipitation radar on the Tropical Rainfall Measuring Mission (Ku band) and the upcoming Global Precipitation Measurement mission satellite's dual-frequency (Ku and Ka bands) precipitation radar. The aircraft measurements of strong hailstorms have been combined with ground-based polarimetric measurements to obtain a better understanding of the response of the Ku- and Ka-band radar to the vertical distribution of the hydrometeors, including hail. Data from two flight lines on 24 May 2011 are presented. Doppler velocities were ~39 m s−1 at 10.7-km altitude from the first flight line early on 24 May, and the lower value of ~25 m s−1 on a second flight line later in the day. Vertical motions estimated using a fall speed estimate for large graupel and hail suggested that the first storm had an updraft that possibly exceeded 60 m s−1 for the more intense part of the storm. This large updraft speed along with reports of 5-cm hail at the surface, reflectivities reaching 70 dB Z at S band in the storm cores, and hail signals from polarimetric data provide a highly challenging situation for spaceborne-radar measurements in intense convective systems. The Ku- and Ka-band reflectivities rarely exceed ~47 and ~37 dB Z, respectively, in these storms. [ABSTRACT FROM AUTHOR]

Data collected by C-band and X-band radars in northwestern Italy are analyzed to study the behavior of the polarimetric variables in the ice region of precipitating clouds, with special emphasis on the specific differential phase Kdp. It is found that stratiform precipitation, irrespective of the precipitation type at the ground and as opposed to convective systems, is characterized by well-pronounced positive differential reflectivity Zdr and Kdp values near the model-predicted −15°C isotherm. The regions of enhanced Zdr and Kdp are likely related to the growth of dendrite crystals in the region where the difference between the saturation vapor pressure over water and the saturation vapor pressure over ice is greatest. Coincident C-band and X-band measurements, in conjunction with electromagnetic scattering simulations, demonstrate that Kdp scales with frequency, indicating that the ice particles in the vapor deposition preferential growth zone are Rayleigh scatterers. Peak values around 2.0° and 3.5° km−1 are observed at C band and X band, respectively. Most noteworthy is that an extended analysis of hourly and daily vertical profiles of C-band data over 1 year has shown that Kdp observations around the −15°C temperature level in stratiform precipitation are well correlated (0.8) with the reflectivity in the underlying rain layer. [ABSTRACT FROM AUTHOR]

Conventional methods of viewing and combining retrieved geophysical fields from polar-orbiting satellites often complicate the work of end users because of the erratic time differences between overpasses, the significant time gaps between elements of a composite image, or simply the different requirements for interpretation between contributing instruments. However, it is possible to mitigate these issues for any number of retrieved quantities in which the tracer lifetime exceeds the sampling time. This paper presents a method that uses ''advective blending'' to create high-fidelity composites of data from polar-orbiting satellites at high temporal resolution, including a characterization of error as a function of time gap between satellite overpasses. The method is especially effective for tracers with lifetimes of longer than 7 h. Examples are presented using microwave-based retrievals of total precipitable water (TPW) over the ocean, from the Cooperative Institute for Meteorological Satellite Studies (CIMSS) Morphed Integrated Microwave Imagery at CIMSS TPW product (MIMIC-TPW). The mean average error of a global 0.25°° ×× 0.25°° product at 1-h resolution is 0.5--2 mm, which is very reasonable for most applications. [ABSTRACT FROM AUTHOR]

Extreme precipitation events are challenging to local and regional stakeholders across the United States. The Missouri River basin (MoRB), covering an area over 1.29 million km2, is prone to extreme precipitation events. These events are exacerbated by the complex terrain in the west and the numerous weather and climate features that impact the region on a seasonal and annual basis (low-level jets, mesoscale convective systems, extreme cold air intrusions, etc.). Without an in-depth analysis of extreme precipitation in the MoRB, the evolving nature of extreme precipitation is not known. This situation warrants an analysis of extreme precipitation, especially relating to subannual variations when extreme precipitation is more impactful. To this end, data from 131 U.S. Historical Climatology Network (USHCN) stations were used to determine the nature of extreme precipitation from 1950 to 2019. Annual 99th-percentile events and annual station maximum precipitation events occur more frequently in the eastern MoRB than in the western MoRB, in line with the annual precipitation climatology. Results show that 99th-percentile events and annual station maximum precipitation events are becoming more frequent across the MoRB. Through analysis of 3-month extreme precipitation trends, areas in the eastern and southern MoRB are shown to have an increase in event frequency and intensity. Frequency shifts in the 99th-percentile events, however, have occurred across the entire region. The increasing frequency of extreme events in the western MoRB represents a significant change for the hydroclimate of the region. Overall, the results from this work show that MORB extreme precipitation has increased in frequency and intensity during the 1950–2019 period. [ABSTRACT FROM AUTHOR]