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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]

The Bay Area of California and surrounding region receives much of its annual precipitation during the October–March wet season, when atmospheric river events bring periods of heavy rain that challenge water managers and may exceed the capacity of storm sewer systems. The complex terrain of this region further complicates the situation, with terrain interactions that are not currently captured in most operational forecast models and inadequate precipitation measurements to capture the large variability throughout the area. To improve monitoring and prediction of these events at spatial and temporal resolutions of interest to area water managers, the Bay Area Advanced Quantitative Precipitation Information project was developed. To quantify improvements in forecast precipitation, model validation studies require a reference dataset to compare against. In this paper we examine 10 gridded, high-resolution (≤10 km, hourly) precipitation estimates to assess the uncertainty of high-resolution quantitative precipitation estimates (QPE) in areas of complex terrain. The products were linearly interpolated to 3-km grid spacing, which is the resolution of the operational forecast model to be validated. Substantial differences exist between the various products at accumulation periods ranging from hourly to annual, with standard deviations among the products exceeding 100% of the mean. While the products seem to agree fairly well on the timing of precipitation, intensity estimates differ, sometimes by an order of magnitude. The results highlight both the need for additional observations and the need to account for uncertainty in the reference dataset when validating forecasts in this area. [ABSTRACT FROM AUTHOR]

The intensity of interannual variation of spring precipitation over southern China during 1979–2014 and possible reasons for it are investigated in this paper. There is a significant interdecadal change in the intensity of interannual variation of spring precipitation over southern China around 1995/96. The intensity of interannual variation of spring rainfall over South China is stronger during 1979–95 than that during 1996–2014. The possible reason may be the larger amplitude of the sea surface temperature anomaly (SSTA) in the western Pacific Ocean (WP) before 1995/96. The cooler (warmer) SSTA in WP may trigger an abnormal local anticyclone (cyclone) at lower levels. The anomalous southwesterly (northeasterly) flow at the northwestern flank of the WP anticyclone (cyclone) covers South China, transporting more (less) moisture to South China. Meanwhile, the anomalous winds converge (diverge) in South China at lower levels and diverge (converge) at upper levels, which causes the anomalous ascent (descent) to enhance (reduce) the precipitation over there. However, during 1996–2014, the intensity of interannual variation of spring rainfall over the middle and lower reaches of the Yangtze River valley becomes much stronger than that during 1979–95, which is related to the intensified interannual variation of the atmospheric circulation in the middle and high latitudes over Eurasia. The weak (strong) Siberian high and East Asian trough may reduce (enhance) the northerly wind from the middle and high latitudes. As a result, the middle and lower reaches of the Yangtze River valley are subjected to the anomalous southerly wind, favoring more (less) precipitation over there. [ABSTRACT FROM AUTHOR]

Numerous circulation indices have been applied in practical climate services focused on regional precipitation. It is beneficial to identify the most influential or decisive indices, but this is difficult with conventional correlation analyses because of the underlying nonlinear mechanisms for precipitation. This paper demonstrates a set of the most influential indices for July–August precipitation in North China, based on the recursive random forest (RRF) method. These decisive circulation indices include the Polar–Eurasia teleconnection, North African subtropical high ridge position, India–Burma trough, Antarctic Oscillation, Northern Hemisphere polar vortex central latitude, North Atlantic Oscillation, and western Pacific subtropical high northern boundary position. Some of these factors have been recognized as directly influential to the regional precipitation, for example, those of the northwestern Pacific subtropical high; however, some are not easily understood. Decision tree (DT) models using these indices were developed to facilitate composite analyses to explain the RRF results. Taking one of the most interesting DT rules as an example, when the North African subtropical high ridge position is sufficiently far south, an anomalous anticyclone occurs in the upstream and an anomalous cyclone in the downstream of North China. This is unfavorable for northward moisture transport in eastern China and hence causes less precipitation in North China than climatology. The present results are not only helpful for improving diagnostic models of regional precipitation, but also enlightening for exploring how global climate change could impact a region by modulating large-scale circulation patterns. [ABSTRACT FROM AUTHOR]

Compared to traditional single-polarization radar, dual-polarization radar has a number of advantages for quantitative precipitation estimation because more information about the drop size distribution and hydrometeor type can be gleaned. In this paper, an improved dual-polarization rainfall methodology is proposed, which is driven by a region-based hydrometeor classification mechanism. The objective of this study is to incorporate the spatial coherence and self-aggregation of dual-polarization observables in hydrometeor classification and to produce robust rainfall estimates for operational applications. The S-band dual-polarization data collected from the NASA Polarimetric (NPOL) radar during the GPM Iowa Flood Studies (IFloodS) ground validation field campaign are used to demonstrate and evaluate the proposed rainfall algorithm. Results show that the improved rainfall method provides better performance than a few single- and dual-polarization algorithms in previous studies. This paper also investigates the impact of radar beam broadening on various rainfall algorithms. It is found that the radar-based rainfall products are less correlated with ground disdrometer measurements as the distance from the radar increases. [ABSTRACT FROM AUTHOR]

This paper evaluates several daily precipitation products over western and central Europe, identifies and documents their respective strengths and shortcomings, and relates these to uncertainties associated with each of the products. We analyze one gauge-based, three satellite-based, and two reanalysis-based products using high-density rain gauge observations as reference. First, we assess spatial patterns and frequency distributions using aggregated statistics. Then, we determine the skill of precipitation event detection from these products with a focus on extremes, using temporally and spatially matched pairs of precipitation estimates. The results show that the quality of the datasets largely depends on the region, season, and precipitation characteristic addressed. The satellite and the reanalysis precipitation products are found to have difficulties in accurately representing precipitation frequency with local overestimations of more than 40%, which occur mostly in dry regions (all products) as well as along coastlines and over cold/frozen surfaces (satellite-based products). The frequency distributions of wet-day intensities are generally well reproduced by all products. Concerning the frequency distributions of wet-spell durations, the satellite-based products are found to have clear deficiencies for maritime-influenced precipitation regimes. Moreover, the analysis of the detection of extreme precipitation events reveals that none of the non-station-based datasets shows skill at the shortest temporal and spatial scales (1 day, 0.25°), but at and above the 3-day and 1.25° scale the products start to exhibit skill over large parts of the domain. Added value compared to coarser-resolution global benchmark products is found both for reanalysis and satellite-based products. [ABSTRACT FROM AUTHOR]

This paper reports a consistent seesaw relationship between interdecadal precipitation variability over North China and the Southwest United States, which can be found in observations and simulations with several models. Idealized model simulations suggest the seesaw could be mainly driven by the interdecadal Pacific oscillation (IPO), through a large-scale circulation anomaly occupying the entire northern North Pacific, while the Atlantic multidecadal oscillation (AMO) contributes oppositely and less. Modulation of precipitation by the IPO tends to be intensified when the AMO is in the opposite phase, but weakened when the AMO is in the same phase. The warm IPO phase is associated with an anomalous cyclone over the northern North Pacific; consequently, anomalous southwesterly winds bring more moisture and rainfall to the Southwest United States, while northwesterly wind anomalies prevail over North China with negative rainfall anomalies. The east–west seesaw of rainfall anomalies reverses sign when the circulation anomaly becomes anticyclonic during the cold IPO phase. The IPO-related tropical SST anomalies affect the meridional temperature gradient over the North Pacific and adjacent regions and the mean meridional circulation. In the northern North Pacific, the atmospheric response to IPO forcing imposes an equivalent barotropic structure throughout the troposphere. An important implication from this study is the potential predictability of drought-related water stresses over these arid and semiarid regions, with the progress of our understanding and prediction of the IPO and AMO. [ABSTRACT FROM AUTHOR]

This paper scrutinizes the zonal oscillation of the western North Pacific subtropical high (WNPSH) via diagnosing its two extreme phases, which are defined by the top 10% strongest (positive phase) and the weakest (negative phase) WNPSH index (WNPSHI) days during summers in 1979–2016. Key findings include the following: a tripole pattern consisting of intensified (weakened) precipitation over the Maritime Continent and the East Asian summer monsoon regions, and suppressed (strengthened) precipitation over the western North Pacific summer monsoon region during positive (negative) WNPSH phases; a westward movement of WNPSH-induced precipitation anomalies that subsequently affects eastern China, Japan, and the Korean Peninsula at different time lags; an OLR–vorticity pattern explained by atmospheric responses to thermal sources is suggested to drive the oscillation; and the competitive interaction of local air–sea feedbacks, especially during the positive phase. In addition, moderate-to-strong positive correlations between the WNPSHI and the Niño-3.4 index are found on 1–2-, 2–3-, and 3–6-yr time scales; both exhibit decadal shifts to a higher-frequency mode, suggesting the intensification of both the zonal WNPSH oscillation and the ENSO under the changing climate and their close interdecadal association. A nonlinear quasi-biennial WNPSH–ENSO relationship is identified: the positive (negative) WNPSH phase sometimes occurs during 1) a decaying El Niño (La Niña) in the preceding summer/autumn, and/or 2) a developing La Niña (El Niño) in the current summer/autumn. A full ENSO transition from moderate-to-strong El Niño to La Niña is often seen during the positive phase, offering potential in predicting ENSO events and extreme WNPSH phases and thereby the summer monsoon rainfall in East Asia. [ABSTRACT FROM AUTHOR]

A prototype quantitative precipitation estimate (QPE) algorithm that utilizes specific attenuation A and specific differential phase KDP was developed for inclusion into the Multi-Radar Multi-Sensor (MRMS) system and the Weather Surveillance Radar-1988 Doppler (WSR-88D) network. Special attention is given to the optimization of the factor α used for computation of a path-integrated attenuation from a total span of differential phase along the propagation path in rain. It is suggested to estimate α from a slope of the ZDR dependence on Z in rain. The use of real-time adjusted α allows us to capture the variations of the drop size distributions, and therefore improve the QPE accuracy. It is demonstrated that the factor α is generally higher for tropical rain type compared to continental rain. Since the R(A) approach is only valid for pure rainfall, the R(KDP) relation is suggested as a complement in areas of hail contamination. The paper contains a description of the basic version of the R(A) and R(KDP) algorithm and recommendations for its further optimization. [ABSTRACT FROM AUTHOR]

Extensive evaluations have been performed on the dual-frequency classification module in the Global Precipitation Mission (GPM) Dual-Frequency Precipitation Radar (DPR) level-2 algorithm. Both rain type classification and melting-layer detection continue to show promising results in the validations. Surface snowfall identification is a feature newly added in the classification module to the recently released version to provide a surface snowfall flag for each qualified vertical profile. This algorithm is developed upon vertical features of Ku- and Ka-band reflectivity and dual-frequency ratio from DPR. In this paper, we validate this surface snowfall identification algorithm with ground radars including NEXRAD, NASA Polarimetric Radar (NPOL), and CSU–CHILL radar during concurrent precipitation events and GPM validation campaign Olympic Mountain Experiment (OLYMPEX). Other ground truth such as Precipitation Imaging Package (PIP) and ground report is also included in the validation. Based on 16 validation cases in the years 2014–18, the average match ratio between surface snowfall flag from space radar and ground radar is around 87.8%. Promising agreements are achieved with different validation sources. Algorithm limitation and potential improvement are discussed. [ABSTRACT FROM AUTHOR]

This paper describes a region-tree-based precipitation forecast verification approach, which is then used to assess the performance of a numerical model forecast at various scales. In this verification approach, the forecast and observation fields are first represented using region-tree structures. The verification approach consists of three procedures: calculation of spatial structural properties of the forecast and observation trees, matching the forecast tree with the observation tree, and deforming the forecast tree to resemble the observation tree. Based on these procedures, the tree-based verification approach aims at determining the spatial structural differences, displacement errors, and intensity errors between the forecast and the observation at different scales. The behavior of the proposed verification approach is investigated by applications to a standard verification dataset from the spatial forecast verification intercomparison project. [ABSTRACT FROM AUTHOR]

This paper examines the synoptic conditions that yield extreme precipitation in two regions with different orographic features, the Olympic Mountains and Puget Sound. To capture orographic extreme precipitation, a dynamical downscaling is performed, driven by the NCEP–NCAR reanalysis and evaluated for cool-season months from 1970 to 2010. Clustering techniques are applied to the regional climate simulation, which reveals the Olympic Mountains and Puget Sound as regions with distinct temporal variability in precipitation. Results show that approximately one-third of the extreme precipitation events in each region occur without a similarly extreme event in the other, in spite of the fact that the two areas are very closely located and one is downstream of the other. Composites of synoptic conditions for extreme precipitation events show differences in integrated vapor transport (IVT) due to its dynamical component (winds at 850 hPa) and its thermodynamical component [integrated water vapor (IWV)]. For Puget Sound events, IVT is lower compared to Olympic Mountain events because of lower wind speeds. Olympic Mountain events have lower IVT compared to events with extreme precipitation in both regions, but in this case, the difference is due to lower IWV and more southerly winds. These differences in the large-scale conditions promote differences in the mesoscale mechanisms that enhance precipitation in each location. For Puget Sound events, static stability is higher, and there is a weak rain shadow. For Olympic Mountain events, static stability is lower, and a strong rain shadow is present. During extreme events in both regions, orographic modulation is minimized and large-scale effects dominate. [ABSTRACT FROM AUTHOR]

Accurate precipitation data are fundamental for understanding and mitigating the disastrous effects of many natural hazards in mountainous areas. Floods and landslides, in particular, are potentially deadly events that can be mitigated with advanced warning, but accurate forecasts require timely estimation of precipitation, which is problematic in regions such as tropical Africa with limited gauge measurements. Satellite rainfall estimates (SREs) are of great value in such areas, but rigorous validation is required to identify the uncertainties linked to SREs for hazard applications. This paper presents results of an unprecedented record of gauge data in the western branch of the East African Rift, with temporal resolutions ranging from 30 min to 24 h and records from 1998 to 2018. These data were used to validate the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) research version and near-real-time products for 3-hourly, daily, and monthly rainfall accumulations, over multiple spatial scales. Results indicate that there are at least two factors that led to the underestimation of TMPA at the regional level: complex topography and high rainfall intensities. The TMPA near-real-time product shows overall stronger rainfall underestimations but lower absolute errors and a better performance at higher rainfall intensities compared to the research version. We found area-averaged TMPA rainfall estimates relatively more suitable in order to move toward regional hazard assessment, compared to data from scarcely distributed gauges with limited representativeness in the context of high rainfall variability. [ABSTRACT FROM AUTHOR]

Recent field observations suggest that cloud-top precipitation generating cells (GCs) are ubiquitous in the warm-frontal and comma-head regions of midlatitude winter cyclones. The presence of fallstreaks emanating from the GCs and their persistence either to the surface or until merging into precipitation bands suggests that GCs are a critical component of the precipitation process in these cyclones. This paper is the second part of a three-part series that investigates the dynamics of GCs through very-high-resolution idealized Weather Research and Forecasting (WRF) Model simulations. This paper assesses the role of cloud-top instability paired with nighttime, daytime, or no radiative forcing on the development and maintenance (or lack) of GCs. Under initially unstable conditions at cloud top, GCs develop regardless of radiative forcing but only persist clearly with radiative forcing. Cloud-top destabilization due to longwave cooling leads to development of GCs even under initially neutral and stable conditions, providing a physical explanation for the observed ubiquity of GCs atop winter cyclones. GCs do not develop in initially stable simulations with no radiation. Decreased range in vertical velocity spectra under daytime radiative forcing is consistent with offset of the destabilizing influence of longwave cooling by shortwave heating. [ABSTRACT FROM AUTHOR]

Precipitation displays a remarkable variability in space and time. An important yet poorly documented aspect of this variability is intermittency. In this paper, a new way of quantifying intermittency based on the burstiness B and memory M of interamount times is proposed. The method is applied to a unique dataset of 325 high-resolution rain gauges in the United States and Europe. Results show that the M- B diagram provides useful insight into local precipitation patterns and can be used to study intermittency over a wide range of temporal scales. It is found that precipitation tends to be more intermittent in warm and dry climates with the largest observed values in the southwest of the United States (i.e., California, Nevada, Arizona, and Texas). Low-to-moderate values are reported for the northeastern United States, the United Kingdom, the Netherlands, and Germany. In the second half of the paper, the new metrics are applied to daily rainfall data for 1954-2013 to investigate regional trends in intermittency due to climate variability and global warming. No evidence is found of a global shift in intermittency but a weak trend toward burstier precipitation patterns and longer dry spells in the south of Europe (i.e., Portugal, Spain, and Italy) and an opposite trend toward steadier and more correlated precipitation patterns in Norway, Sweden, and Finland is observed. [ABSTRACT FROM AUTHOR]

In a series of two papers, rain-rate retrievals based on specific attenuation A at radar X-band wavelength using the R( A) method presented by Ryzhkov et al. are thoroughly investigated. Continuous time series of overlapping measurements from two polarimetric X-band weather radars in Germany during the summers of 2011-13 are used to analyze various aspects of the method, like miscalibration correction, ground clutter contamination, partial beam blockage (PBB), sensitivity to precipitation characteristics, and sensitivity to temperature assumptions in the retrievals. In Part I of the series, the relations inherent to the R( A) method were used to calculate radar reflectivity Z from specific attenuation and it was compared with measured reflectivity to estimate PBB and calibration errors for both radars. In this paper, R( A) rain estimates are compared to R( Z) and R( KDP) retrievals using specific phase shift KDP. PBB and calibration corrections derived in Part I made the R( Z) rainfall estimates almost perfectly consistent. Accumulated over five summer months, rainfall maps showed strong effects of clutter contamination if R( KDP) is used and weaker impact on R( A). These effects could be reduced by processing the phase shift measurements with more resilience toward ground clutter contamination and by substituting problematic R( KDP) or R( A) estimates with R( Z). Hourly and daily accumulations from rain estimators are compared with rain gauge measurements; the results show that R( A) complemented by R( Z) in segments with low total differential phase shift correlates best with gauges and has the lowest bias and RMSE, followed by R( KDP) substituted with R( Z) at rain rates below 8 mm h−1. [ABSTRACT FROM AUTHOR]

The release of seasonal (and longer) predictions of various climatological quantities is now routine. While undoubtedly devastating to lives and livelihoods, it is unclear whether seasonal extremes in precipitation-for example, extreme dry spells leading to droughts or heavy precipitation events leading to flooding-represent a feasible target for these predictions, that is, whether they are potentially predictable or are instead inherently unpredictable more than a few days to weeks in advance. This paper assesses the potential for predicting seasonal extremes in observed precipitation as a function of region and time of year by decomposing the station-based variance into that attributable to short-memory behavior of typical meteorological events-as generated from station-specific, seasonally varying, daily time-scale stationary stochastic weather models (SSWMs)-and that attributable to longer-time-scale, potentially predictable changes in precipitation-producing processes. Findings suggest the potential for making skillful predictions of seasonal precipitation extremes over the United States is enhanced (reduced) during the cool (warm) season, particularly for heavy precipitation event accumulations. Further, this potential is accentuated along the West Coast, around the Great Lakes, and over the central plains and Ohio River valley but is diminished over the Northeast and northern Great Plains. However, findings also suggest the potential for producing seasonal (and longer) predictions of seasonal precipitation extremes is spatially and seasonally dependent. As such, this paper includes supplemental material for the potentially predictable variance of seasonal extreme dry spell lengths, heavy event accumulations, and total accumulations at 774 stations across all 365 days so readers can evaluate the potential predictability for the location, timing, and metric of most relevance to them. [ABSTRACT FROM AUTHOR]

In a two-part paper, radar rain-rate retrievals using specific attenuation A suggested by Ryzhkov et al. are thoroughly investigated. Continuous time series of overlapping measurements from two twin polarimetric X-band weather radars in Germany during the summers of 2011-13 are used to analyze various aspects of rain-rate retrieval, including miscalibration correction, mitigation of ground clutter contamination and partial beam blockage (PBB), sensitivity to precipitation characteristics, and the temperature assumptions of the R( A) technique. In this paper, the relations inherent to the R( A) method are used to estimate radar reflectivity Z from A and compare it to the measured Z in order to estimate PBB and calibration offsets for both radars. The fields of Z estimated from A for both radars are consistent, and the differences between Z( A) and measured Z are in good agreement with the ones calculated using either consistency relations between reflectivity at horizontal polarization Z H, differential reflectivity ZDR, and specific differential phase KDP in rain or a digital elevation model in the presence of PBB. In the analysis, the dependence of A on temperature appears to have minimal effects on the overall performance of the method. As expected, the difference between Z( A) and attenuation-corrected measured Z observations varies with rain type and exhibits a weak systematic dependency on rainfall intensity; thus, averaging over several rain events is required to obtain reliable estimates of the Z biases caused by radar miscalibration and PBB. [ABSTRACT FROM AUTHOR]

Discrepancies between Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Precipitation Radar (PR) oceanic rainfall retrievals are prevalent between El Niño and La Niña conditions with TMI exhibiting systematic shifts in precipitation. To investigate the causality of this relationship, this paper focuses on the evolution of precipitation organization between El Niño and La Niña and their impacts on TRMMprecipitation. The results indicate that discrepancies are related to shifts from isolated deep convection during La Niña toward organized precipitation duringElNiñowith the largest variability occurring in the Pacific basins. During El Niño, organized systems are more frequent, have increased areal coverage of stratiform rainfall, and penetrate deeper into the troposphere compared to La Niña. The increased stratiform raining fraction leads to larger increases inTMI rain rates than PR rain rate retrievals.Reanalysis and water vapor data from the Atmospheric Infrared Sounder (AIRS) indicate that organized systems are aided by midtropospheric moisture increases accompanied by increased convective frequency. During La Niña, tropical rainfall is dominated by isolated deep convection due to drier midtropospheric conditions and strong mid- and upper-level zonal wind shear. To examine tropical rainfall--sea surface temperature relations, regime-based bias corrections derived using ground validation (GV) measurements are applied to the TRMM rain estimates. The robust connection with GV-derived biases and oceanic precipitation leads to a reduction in TMI-PR regional differences and tropics-wide precipitation anomalies.The improved agreement betweenPRandTMI estimates yields positive responses of precipitation to tropical SSTs of 10% 8C-1 and 17% 8C-1, respectively, consistent with 15% 8C-1 from the Global Precipitation Climatology Project (GPCP). [ABSTRACT FROM AUTHOR]

The IPCC Fifth Assessment Report highlighted large uncertainty in European precipitation changes in the coming century. This paper investigates the sources of intermodel differences using CMIP5 model European precipitation data. The contribution of atmospheric circulation to differences in precipitation trends is investigated by applying cluster analysis to daily mean sea level pressure (MSLP) data. The resulting classification is used to reconstruct monthly precipitation time series, thereby isolating the component of precipitation variability directly related to atmospheric circulation. Reconstructed observed precipitation and reconstructions of simulated historical and projection data are well correlated with the original precipitation series, showing that circulation variability accounts for a substantial fraction of European precipitation variability. Removing the reconstructed precipitation from the original precipitation leaves a residual component related to noncirculation effects (and any small remaining circulation effects). Intermodel spread in residual future European precipitation trends is substantially reduced compared to the spread of the original precipitation trends. Uncertainty in future atmospheric circulation accounts for more than half of the intermodel variance in twenty-first-century precipitation trends for winter months for both northern and southern Europe. Furthermore, a substantial part of this variance is related to different forced dynamical responses in different models and is therefore potentially reducible. These results highlight the importance of understanding future changes in atmospheric dynamics in achieving more robust projections of regional climate change. Finally, the possible dynamical mechanisms that may drive the future differences in regional circulation and precipitation are illustrated by examining simulated teleconnections with tropical precipitation. [ABSTRACT FROM AUTHOR]

Coherent power is an alternative to the conventional noise-subtracted power technique for measuring weather radar signal power. The inherent noise-canceling feature of coherent power eliminates the need for estimating and subtracting the noise component, which is required when performing conventional signal power estimation at low signal-to-noise ratio. The coherent power technique is particularly useful when averaging a high number of samples to improve sensitivity to weak signals. In such cases, the signal power is small compared to the noise power and the required accuracy of the estimated noise power may be difficult to achieve. This paper compares conventional signal power estimation with the coherent power measurement technique by investigating bias, standard deviation, and probability of false alarm and detection rates as a function of signal-to-noise ratio and threshold level. This comparison is performed using analytical expressions, numerical simulations, and analysis of cloud and precipitation data collected with the airborne solid-state Ka-band precipitation radar (KPR) operated by the University of Wyoming. [ABSTRACT FROM AUTHOR]

A new passive microwave rainfall retrieval algorithm for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) that relies on an a priori database derived from matchups between TMI brightness temperatures and precipitation radar (PR)-derived surface rain rates has been developed. In addition to implementing a fairly conventional Bayesian approach to precipitation estimation, it exploits a dimensional reduction technique designed to increase the effective sample density in the database and also to improve the detectability of precipitation over problem surface types. The details of the algorithm itself are described in a companion paper. In this paper, the algorithm is validated against independent PR-TMI matchups from calendar year 2002. The validation results are benchmarked against results obtained for the same scenes from the current standard (version 7) 2A12 rainfall product for TRMM. Validation statistics considered include the biases, correlation coefficients, and root-mean-square (RMS) differences for annual precipitation totals on a 1° grid as well as two-threshold Heidke skill scores (HSS) for instantaneous (pixel level) retrievals, determined separately for each of seven surface classes, including ocean, coast, and five other basic land surface types as well as for cold (<275 K) and warm surface skin temperatures. Overall, the University of Wisconsin (UW) algorithm exhibits markedly reduced RMS error and bias in the annual total rainfall and markedly improved instantaneous skill at delineating light rain rates, especially over land and the coast. To ensure that the improved results were not due to both the training and validation data having been taken from the same calendar year, the validation of the UW algorithm is repeated using 2005 matchup data. [ABSTRACT FROM AUTHOR]

Global monthly rainfall estimates and other hydrological products have been produced from 1987 to the present using measurements from the Defense Meteorological Satellite Program (DMSP) series of the Special Sensor Microwave Imager (SSM/I). The aim of this paper is twofold: to present the recent efforts to improve the quality control (QC) of historical antenna temperature of the SSM/I sensor (1987-2008) and how this improvement impacts the different hydrological products that are generated at NOAA/National Environmental Satellite, Data, and Information Service (NESDIS). Beginning in 2005, the DMSP Special Sensor Microwave Imager/Sounder (SSMI/S) has been successfully operating on the F-16, F-17, and F-18 satellites. The second objective of this paper is focused on the application of SSMI/S channels to evaluate the performance of several hydrological products using the heritage of existing SSM/I algorithms and to develop an improved strategy to extend the SSM/I time series into the SSMI/S era, starting with data in 2009 for F-17. The continuity of hydrological products from SSM/I to SSMI/S has shown to be a valuable contribution for the precipitation and climate monitoring community but several sensor issues must be accounted for to meet this objective. [ABSTRACT FROM AUTHOR]

Rain evaporation, while significantly contributing to moisture and heat cloud budgets, is a still poorly understood process with few measurements presently available. Multiwavelength lidars, widely employed in aerosols and clouds studies, can also provide useful information on the microphysical characteristics of light precipitation, for example, drizzle and virga. In this paper, lidar measurements of the median volume raindrop diameter and rain evaporation rate profiles are compared with a model analytical solution. The intercomparison reveals good agreement between the model and observations, with a correlation between the profiles up to 65% and a root-mean-square error up to 22% with a 5% bias. Larger discrepancies are due to radiosonde soundings different air masses and model assumptions no more valid along the profile as nonsteady atmosphere and/or appearance of collision-coalescence processes. Nevertheless, this study shares valuable information to better characterize the rain evaporation processes. [ABSTRACT FROM AUTHOR]

The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), features several major advances from the original MERRA reanalysis, including the use, outside of high latitudes, of observations-based precipitation data products to correct the precipitation falling on the land surface in the MERRA-2 system. The method for merging the observed precipitation into MERRA-2 has been refined from that of the (land-only) MERRA-Land reanalysis. This paper describes the method and evaluates the MERRA-2 land surface precipitation. Compared to monthly GPCPv2.2 observations, the corrected MERRA-2 precipitation (M2CORR) is better than the precipitation generated by the atmospheric models within the cycling MERRA-2 and MERRA systems. M2CORR is also better than MERRA-Land precipitation over Africa because in MERRA-2 a merged satellite-gauge precipitation product is used instead of the gauge-only data used for MERRA-Land. Compared to 3-hourly TRMM observations, the M2CORR diurnal cycle has better amplitude but less realistic phasing than MERRA-2 model-generated precipitation. Because correcting the precipitation within the coupled atmosphere-land modeling system allows the MERRA-2 near-surface air temperature and humidity to respond to the improved precipitation forcing, MERRA-2 provides more self-consistent surface meteorological data than were available from MERRA-Land, which is important for applications such as land-only modeling studies. Where precipitation observations of sufficient quality are available for use in the reanalysis, the corrections facilitate the seamless spinup of the land surface initial conditions across the MERRA-2 production streams. At high latitudes, however, the lack of reliable precipitation observations results in undesirable land spinup effects that impact mostly the first published year of each MERRA-2 stream (1980, 1992, 2001, and 2011). [ABSTRACT FROM AUTHOR]

This paper presents a novel, simple method to correct reflectivity measurements of weather radars that operate in attenuation-influenced frequency bands using observations from less attenuated radar systems. In recent years radar systems operating in the X-band frequency range have been developed to provide precipitation fields for areas of special interest in high temporal (≤1 min) and spatial (≤250 m) resolution in complement to nationwide radar networks. However, X-band radars are highly influenced by attenuation. C- and S-band radars typically have coarser resolution (250 m-1 km and 5 min) but are less affected by attenuation. Correcting for attenuation effects in simple (non-Doppler) single-polarized X-band radars remains challenging and is often dependent on restriction parameters, for example, those derived from mountain returns. Therefore, these algorithms are applicable only in limited areas. The method proposed here uses measurements from C-band radars and hence can be applied in all regions covered by nationwide C- (or S-) band radar networks. First, a single scan of X-band radar measurements is used exemplary to identify advantages and disadvantages of the novel algorithm compared to a standard single radar algorithm. The performance of the correction algorithms in different types of precipitation is examined in nine case studies. The proposed method provides very promising results for each type of precipitation. Additionally, it is evaluated in a 5-month comparison with Micro Rain Radar (MRR) observations. The bias between uncorrected X-band radar and MRR data is nearly eliminated by the attenuation correction algorithm, and the RMSE is reduced by 20% while the correlation of ~0.9 between both systems remains nearly constant. [ABSTRACT FROM AUTHOR]

The goal of this study is to utilize several recent developments on rainfall data collection, downscaling of available climate models, training and forecasts from such models within the framework of a multimodel superensemble, and first a detailed examination of the seasonal climatology. The unique aspect of this study is that it became possible to use the forecast results from as many as 16 state-of-the-art coupled climate models. A downscaling component, with respect to observed rainfall estimates, uses a very dense Asian rain gauge network. This feature enables the forecasts of each model to be bias corrected to a common 25-km resolution. The downscaling statistics for each model, at each grid location, are developed during a training phase of the model forecasts. This is done wherever the observed rainfall estimates are available. In the 'forecast phase,' the forecasts from all of the member models use the downscaling coefficients of the 'training phase.' The downscaling and the extraction of the superensemble weights are done during the training phase. This makes use of the cross-validation principle. This means that the season to be forecasted is left out of the entire forecast dataset. Thus all of the statistics for downscaling and the superensemble construction are done separately for the forecasts of each season for all the years. The forecast phase is the season that is being forecast, where the aforementioned statistics are deployed for constructing the final downscaled superensemble. These forecasts are next used for the construction of a multimodel superensemble. The geographical distributions of the downscaling coefficients provide a first look at the systematic errors of the member model forecasts. This combination of multimodels, the vast rain gauge dataset, the downscaling, and the superensemble provides a major improvement for the rainfall climatology and anomalies for the forecast phase. One of the main results of this paper is on the improvement of rainfall climatology of the member models. The downscaled multimodel superensemble shows a correlation of nearly 1.0 with respect to the observed climatology. This high skill is important for addressing the rainfall anomaly forecasts, which are defined in terms of departures from the observed (rather than a model based) climatology. This first part of the paper provides a description of the member models, the length of the training and forecast phases, the sensitivity of results as the numbers of forecast models are increased, and the skills of the downscaled climatology forecasts. [ABSTRACT FROM AUTHOR]

This is the second part of a paper on the improved seasonal precipitation forecasts for the Asian monsoon using 16 atmosphere-ocean coupled models. This study utilizes a large suite of coupled atmosphere-ocean models; this second part largely addresses the skill of rainfall anomaly forecasts. These include both deterministic and probabilistic skill measures such as the RMS errors, anomaly correlations, equitable threat scores, and the Brier skill score. It was possible to improve the skills of rainfall climatology from the use of a downscaled multimodel superensemble to very high levels, and it is of interest to ask how far this methodology would go toward improving the skills of seasonal rainfall anomaly forecasts. It is possible to go through a sequence of multimodel post processing to improve upon these skills by using a dense rain gauge network over Asia, downscaling forecasts for each member model, and constructing a multimodel superensemble that benefits from the persistence of errors of the member models. This paper addresses the spinup issues of the downscaling and the superensemble results where the number of years of model data needed for training phase, for the downscaling, and for the construction of the superensemble, is addressed. In the context of cross validation, the training phase includes 14 seasons of monsoon data. The forecast phase is only one season; it is this season that was not included in the training phase each time. The relationship between data length and the number of models needed for enhanced skills is another issue that is addressed. Seasonal climate forecasts over the larger monsoon Asia domain and over the regional belts are evaluated. The superensemble forecasts invariably have the highest skill compared to the member models globally and regionally. This is largely due to the presence of large systematic errors in models that carry low seasonal prediction skills. Such models carry persistent signatures of systematic errors, and their errors are recognized by the multimodel superensemble. The probabilistic skills show that the superensemble-based forecasts carry a much higher reliability score compared to the member models. This implies that the superensemble-based forecasts are the most reliable among all the member models. It is possible to examine the performance of models and of the superensemble during periods of heavy monsoon rainfall versus those for deficient monsoon rainfall seasons. One of the conclusions of this study is that given the uncertainties in current modeling for seasonal rainfall forecasts, post processing of multimodel forecasts, using the superensemble methodology, seems to provide the most promising results for the rainfall anomaly forecasts. These results are confirmed by an additional skill metric where the RMS errors and the correlations of forecast skills are evaluated using a normalized precipitation anomaly for the forecasts and the observed estimates. [ABSTRACT FROM AUTHOR]

Middle and eastern routes of the South-North Water Diversion Project (SNWDP) of China, which are approximately located within the area 28°-42°N and 110°-122°E, are being constructed. This paper investigates the past climatic variations on various time scales using instrumental and proxy data. It is found that annual mean surface air temperature has increased significantly during the past 50-100 years, and winter and spring temperatures in the northern part of the region have undergone the most significant changes. A much more significant increase occurs for annual mean minimum temperature and extreme low temperature than for annual mean maximum temperature and extreme high temperature. No significant trend in annual precipitation is found for the region as a whole for the last 50 and 100 years, although obvious decadal and spatial variation is detectable. A seesaw pattern of annual and summer precipitation variability between the north and the south of the region is evident. Over the last 100 years, the Haihe River basin has witnessed a significant negative trend of annual precipitation, but no similar trend is detected for the Yangtze and Huaihe River basins. Pan evaporation has significantly decreased since the mid-1960s in the region in spite of the fact that the trend appears to have ended in the early 1990s. The negative trend of pan evaporation is very significant in the plain area between the Yangtze and Yellow Rivers. There was a notable series of dry intervals lasting decades in the north of the region. The northern drought of the past 30 years is not the most severe in view of the past 500 years; however, the southern drought during the period from the 1960s to the 1980s may have been unprecedented. The dryness-wetness index (DWI) shows significant oscillations with periodicities of 9.5 and 20 years in the south and 10.5 and 25 years in the north. Longer periodicities in the DWI series include 160-170- and 70-80-yr oscillations in the north, and 100-150-yr oscillations in the south. The observed climate change could have implications for the construction and management of the SNWDP. The official approval and start of the hydro project was catalyzed by the severe multiyear drought of 1997-2003 in the north, and the operation and management of the project in the future will also be influenced by climate change-in particular by precipitation variability. This paper provides a preliminary discussion of the potential implications of observed climate change for the SNWDP. [ABSTRACT FROM AUTHOR]

In a changing climate, there is an interest in predicting how extreme rainfall events may change. Using historical records, several recent papers have evaluated whether high-intensity precipitation scales with temperature in accordance with the Clausius--Clapeyron (C--C) relationship. For varying locations in Europe, these papers have identified both super C--C relationships as well as a breakdown of the C--C relationship under dry conditions. In this paper, a similar analysis is carried out for the United States using data from 14 weather stations clustered in four different hydroclimatic regions: the coastal northeast, interior New York, the central plains, and the western plains. In all regions except interior New York state, 99th percentile 1-h precipitation generally followed the C--C relation. In interior New York, there was evidence that intensity scaled with a super C--C relationship. For the 99.9th percentile precipitation, interior New York displayed some moderate evidence of a super C--C relationship, the western plains showed little relation between precipitation and temperature, and the remainder of sites generally scaled with the C--C relationship. Also, if only July, August, and September precipitation is considered, all stations except those in interior New York have little relation between temperature and precipitation, suggesting that precipitation intensity during summer months may not be well constrained by the C--C relationship. Overall, the C--C relationship (or a variation thereof) does not appear to constrain extreme precipitation in all regions and in all seasons, and its ability to aid in constraining future predictions of extreme precipitation may only be relevant to certain locales and time periods. [ABSTRACT FROM AUTHOR]

This paper describes improvements to the Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI) land rainfall algorithm in version 7 (v7) of the TRMM data products. The correlations between rain rates and TMI 85-GHz brightness temperatures (Tb) for convective and stratiform rain are generated using 7 years of collocated TMI and TRMM precipitation radar (PR) data. The TMI algorithm for estimating the convective ratio of rainfall is also modified. This paper highlights both the improvements in the v7 algorithm and the continuing problems with the land rainfall retrievals. It is demonstrated that the proposed changes to the algorithm significantly lower the overestimation by TMI globally and over large sections of central Africa and South America. Also highlighted are the problems with the 2A12 land algorithm that have not been addressed in the version 7 algorithm, such as large regional and seasonal dependence of biases in the TMI rain estimates, and potential changes to the algorithm to resolve these problems are discussed. [ABSTRACT FROM AUTHOR]

In an attempt to develop a better simulation of the climatology of monsoon precipitation in climate models, this paper investigates the impacts of different convective closures on systematic biases of an Indian monsoon precipitation climatology in a high-resolution regional climate model. For this purpose, the Weather Research Forecast (WRF) model is run at 45- and 15-km (two-way nested) resolution with three convective parameterization schemes, namely the Grell–Devenyi (GD), the Betts–Miller–Janjić (BMJ), and the Kain–Fritsch (KF), for the period 1 May–31 October 2001–07. The model is forced with the NCEP–NCAR reanalysis data as the initial and boundary conditions. The simulated June–September (JJAS) mean monsoon rainfall with the three convective schemes is compared with the observations. KF is found to have a high moist bias over the central and western coastal Indian region while GD shows the opposite. Among the three, BMJ is able to produce a reasonable mean monsoon pattern. In an attempt to get further insight into the seasonal bias and its evolution, the probability distribution function (PDF) of different rain-rate categories and their percentage contribution to the seasonal total are computed. BMJ and KF underestimate the observations for lighter rain rates and overestimate for rain-rate categories of more than 10 mm day−1. GD shows an overestimation for lighter rain and an underestimation of PDF for moderate categories. The seasonal patterns of evolution of PDF plots of three rain-rate categories are analyzed to determine whether the convective schemes show any systematic bias throughout the season or if they have problems during certain phases of the monsoon. This shows that the GD systematically overestimates the lighter rain rate and underestimates the moderate rain rate throughout the season, whereas BMJ and KF have problems in the initial stages. The heavy rain category is systematically overestimated by the KF compared to the other two. To further evaluate the proportionate contribution of each rain-rate bin to the total rain, the percentage contribution of each rain rate to the seasonal total is computed. Analyzing all the rain-rate simulations produced by the three schemes, it is found that KF has a moist bias and GD has a dry bias in the spatiotemporal distribution of the monsoon precipitation. Further, this paper investigates the causes behind the mean monsoon precipitation bias. It is shown that GD produces a model climate where the vertical velocity is less than that of the observations up to 500 hPa and the vertically integrated moist instability is also weaker. KF, on the other hand, shows a higher than the observed vertical velocity and a stronger moist instability. Along with this, the vertical profile of heating suggests a warmer middle level in the KF case and significantly reduced midlevel heating for GD. Thus, KF (GD) has produced a model atmosphere that has a stronger (weaker) convective instability to produce the observed bias in the model precipitation. BMJ is found to simulate a reasonable heating profile, along with the realistic moist instability and seasonal cycle of evaporation and condensation. Insight derived from the analysis is expected to help improve the convective parameterizations. [ABSTRACT FROM AUTHOR]

The upper Sacramento River watershed is vital to California's water supply and is susceptible to major floods. Orographic precipitation in this complex terrain involves both atmospheric rivers (ARs) and the Sierra barrier jet (SBJ). The south-southeasterly SBJ induces orographic precipitation along south-facing slopes in the Mt. Shasta-Trinity Alps, whereas landfalling ARs ascend up and over the statically stable SBJ and induce orographic precipitation along west-facing slopes in the northern Sierra Nevada. This paper explores the occurrence of extreme daily precipitation (EDP) in this region in association with landfalling ARs and the SBJ. The 50 wettest days (i.e., days with EDP) for water years (WYs) 2002-11 based on the average of daily precipitation from eight rain gauges known as the Northern Sierra 8-Station Index (NS8I) are compared to dates from an SSM/I satellite-based landfalling AR-detection method and dates with SBJ events identified from nearby wind profiler data. These 50 days with EDP accounted for 20% of all precipitation during the 10-WY period, or 5 days with EDP per year on average account for one-fifth of WY precipitation. In summary, 46 of 50 (92%) days with EDP are associated with landfalling ARs on either the day before or the day of precipitation, whereas 45 of 50 (90%) days with EDP are associated with SBJ conditions on the day of EDP. Forty-one of 50 (82%) days with EDP are associated with both a landfalling AR and an SBJ. The top 10 days with EDP were all associated with both a landfalling AR and an SBJ. [ABSTRACT FROM AUTHOR]

The rain/no-rain classification for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) fails to detect rain over coasts, where the microwave footprint encompasses a mixture of radiometrically cold ocean and radiometrically warm land. A static land-ocean-coast mask is used to determine the surface type of each satellite footprint. The coast mask is conservatively wide to account for the largest footprints, preventing use of the more appropriate ocean or land algorithm for coastal regions. The purpose of this paper is to develop a classification whereby the smallest region possible is defined as coast. In this endeavor, two major improvements are applied to the land-ocean-coast classification. First, the surface classification based on microwave footprints of the high frequency actually used in rain detection is employed. Second, the footprint area of the surface classification is established using an effective field-of-view size and scan geometry of the TMI. These improvements are applied to the Global Satellite Mapping of Precipitation TMI algorithm. The classification result is validated using the TRMM precipitation radar. The validation shows that these improvements lead to better rain detection in the coastal region. [ABSTRACT FROM AUTHOR]

The National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellite series R (GOES-R) will greatly expand the ability to observe the earth from geostationary orbit compared to the current-generation GOES, with more than 3 times as many spectral bands and a 75% reduction in footprint size. These enhanced capabilities are beneficial to rainfall rate estimation since they provide sensitivity to cloud-top properties such as phase and particle size that cannot be achieved using the limited channel selection of current GOES. The GOES-R rainfall rate algorithm, which is an infrared-based algorithm calibrated in real time against passive microwave rain rates, has been previously described in an algorithm theoretical basis document (ATBD); this paper describes modifications since the release of the ATBD, including a correction for evaporation of precipitation in dry regions and improved calibration updates. These improvements have been evaluated using a simplified version applicable to current-generation GOES to take advantage of the high-resolution ground validation data routinely available over the conterminous United States. Correcting for subcloud evaporation using relative humidity from a numerical model reduced false alarm rainfall by half and reduced the overall error by 35% for hourly accumulations validated against the National Centers for Environmental Prediction stage IV radar-gauge field; however, the number of missed events did increase slightly. Reducing the size of the calibration regions and increasing the training data requirements improved the consistency of the retrieved rates in time and space and reduced the overall error by an additional 4%. [ABSTRACT FROM AUTHOR]

This paper assesses the influence of radiative forcing and latent heating on the development and maintenance of cloud-top generating cells (GCs) in high-resolution idealized Weather Research and Forecasting Model simulations with initial conditions representative of the vertical structure of a cyclone observed during the Profiling of Winter Storms campaign. Simulated GC kinematics, structure, and ice mass are shown to compare well quantitatively with Wyoming Cloud Radar, cloud probe, and other observations. Sensitivity to radiative forcing was assessed in simulations with longwave-only (nighttime), longwave-and-shortwave (daytime), and no-radiation parameterizations. The domain-averaged longwave cooling rate exceeded 0.50 K h−1 near cloud top, with maxima greater than 2.00 K h−1 atop GCs. Shortwave warming was weaker by comparison, with domain-averaged values of 0.10-0.20 K h−1 and maxima of 0.50 K h−1 atop GCs. The stabilizing influence of cloud-top shortwave warming was evident in the daytime simulation's vertical velocity spectrum, with 1% of the updrafts in the 6.0-8.0-km layer exceeding 1.20 m s−1, compared to 1.80 m s−1 for the nighttime simulation. GCs regenerate in simulations with radiative forcing after the initial instability is released but do not persist when radiation is not parameterized, demonstrating that radiative forcing is critical to GC maintenance under the thermodynamic and vertical wind shear conditions in this cyclone. GCs are characterized by high ice supersaturation (RHice > 150%) and latent heating rates frequently in excess of 2.00 K h−1 collocated with vertical velocity maxima. Ice precipitation mixing ratio maxima of greater than 0.15 g kg−1 were common within GCs in the daytime and nighttime simulations. [ABSTRACT FROM AUTHOR]

This paper demonstrates the impact of the enhancement in detectability by the dual-frequency precipitation radar (DPR) on board the Global Precipitation Measurement (GPM) core observatory. By setting two minimum detectable reflectivities-12 and 18 dB Z-artificially to 6 months of GPM DPR measurements, the precipitation occurrence and volume increase by ~21.1% and ~1.9%, respectively, between 40°S and 40°N. GPM DPR is found to be able to detect light precipitation, which mainly consists of two distinct types. One type is shallow precipitation, which is most significant for convective precipitation over eastern parts of subtropical oceans, where deep convection is typically suppressed. The other type is probably associated with lower parts of anvil clouds associated with organized precipitation systems. While these echoes have lower reflectivities than the official value of the minimum detectable reflectivity, they are found to mostly consist of true precipitation signals, suggesting that the official value may be too conservative for some sort of meteorological analyses. These results are expected to further the understanding of both global energy and water budgets and the diabatic heating distribution. [ABSTRACT FROM AUTHOR]

Using satellite measurements in microwave bands to retrieve precipitation over land requires proper discrimination of the weak rainfall signals from strong and highly variable background Earth surface emissions. Traditionally, land retrieval methods rely on a weak signal of rainfall scattering on high-frequency channels and make use of empirical thresholding and regression-based techniques. Because of the increased surface signal interference, retrievals over radiometrically complex land surfaces-snow-covered lands, deserts, and coastal areas-are particularly challenging for this class of retrieval techniques. This paper evaluates the results by the recently proposed Shrunken Locally Linear Embedding Algorithm for Retrieval of Precipitation (ShARP) using data from the Tropical Rainfall Measuring Mission (TRMM) satellite. The study focuses on a radiometrically complex region, partly covering the Tibetan highlands, Himalayas, and Ganges-Brahmaputra-Meghna River basins, which is unique in terms of its diverse land surface radiation regime and precipitation type, within the TRMM domain. Promising results are presented using ShARP over snow-covered land surfaces and in the vicinity of coastlines, in comparison with the land rainfall retrievals of the standard TRMM 2A12, version 7, product. The results show that ShARP can significantly reduce the rainfall overestimation due to the background snow contamination and markedly improve detection and retrieval of rainfall in the vicinity of coastlines. During the calendar year 2013, compared to TRMM 2A25, it is demonstrated that over the study domain the root-mean-square difference can be reduced up to 38% annually, while the improvement can reach up to 70% during the cold months of the year. [ABSTRACT FROM AUTHOR]

Assimilation of satellite precipitation data into numerical models presents several difficulties, with two of the most important being the non-Gaussian error distributions associated with precipitation, and large model and observation errors. As a result, improving the model forecast beyond a few hours by assimilating precipitation has been found to be difficult. To identify the challenges and propose practical solutions to assimilation of precipitation, statistics are calculated for global precipitation in a low-resolution NCEP Global Forecast System (GFS) model and the TRMM Multisatellite Precipitation Analysis (TMPA). The samples are constructed using the same model with the same forecast period, observation variables, and resolution as in the follow-on GFS/TMPA precipitation assimilation experiments presented in the companion paper. The statistical results indicate that the T62 and T126 GFS models generally have positive bias in precipitation compared to the TMPA observations, and that the simulation of the marine stratocumulus precipitation is not realistic in the T62 GFS model. It is necessary to apply to precipitation either the commonly used logarithm transformation or the newly proposed Gaussian transformation to obtain a better relationship between the model and observational precipitation. When the Gaussian transformations are separately applied to the model and observational precipitation, they serve as a bias correction that corrects the amplitude-dependent biases. In addition, using a spatially and/or temporally averaged precipitation variable, such as the 6-h accumulated precipitation, should be advantageous for precipitation assimilation. [ABSTRACT FROM AUTHOR]

The estimation of precipitation across the globe from satellite sensors provides a key resource in the observation and understanding of our climate system. Estimates from all pertinent satellite observations are critical in providing the necessary temporal sampling. However, consistency in these estimates from instruments with different frequencies and resolutions is critical. This paper details the physically based retrieval scheme to estimate precipitation from cross-track (XT) passive microwave (PM) sensors on board the constellation satellites of the Global Precipitation Measurement (GPM) mission. Here the Goddard profiling algorithm (GPROF), a physically based Bayesian scheme developed for conically scanning (CS) sensors, is adapted for use with XT PM sensors. The present XT GPROF scheme utilizes a model-generated database to overcome issues encountered with an observational database as used by the CS scheme. The model database ensures greater consistency across meteorological regimes and surface types by providing a more comprehensive set of precipitation profiles. The database is corrected for bias against the CS database to ensure consistency in the final product. Statistical comparisons over western Europe and the United States show that the XT GPROF estimates are comparable with those from the CS scheme. Indeed, the XT estimates have higher correlations against surface radar data, while maintaining similar root-mean-square errors. Latitudinal profiles of precipitation show the XT estimates are generally comparable with the CS estimates, although in the southern midlatitudes the peak precipitation is shifted equatorward while over the Arctic large differences are seen between the XT and the CS retrievals. [ABSTRACT FROM AUTHOR]

The Goddard profiling algorithm has evolved from a pseudoparametric algorithm used in the current TRMM operational product (GPROF 2010) to a fully parametric approach used operationally in the GPM era (GPROF 2014). The fully parametric approach uses a Bayesian inversion for all surface types. The algorithm thus abandons rainfall screening procedures and instead uses the full brightness temperature vector to obtain the most likely precipitation state. This paper offers a complete description of the GPROF 2010 and GPROF 2014 algorithms and assesses the sensitivity of the algorithm to assumptions related to channel uncertainty as well as ancillary data. Uncertainties in precipitation are generally less than 1%-2% for realistic assumptions in channel uncertainties. Consistency among different radiometers is extremely good over oceans. Consistency over land is also good if the diurnal cycle is accounted for by sampling GMI product only at the time of day that different sensors operate. While accounting for only a modest amount of the total precipitation, snow-covered surfaces exhibit differences of up to 25% between sensors traceable to the availability of high-frequency (166 and 183 GHz) channels. In general, comparisons against early versions of GPM's Ku-band radar precipitation estimates are fairly consistent but absolute differences will be more carefully evaluated once GPROF 2014 is upgraded to use the full GPM-combined radar-radiometer product for its a priori database. The combined algorithm represents a physically constructed database that is consistent with both the GPM radars and the GMI observations, and thus it is the ideal basis for a Bayesian approach that can be extended to an arbitrary passive microwave sensor. [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]

Ensemble-based data assimilation techniques are often applied to land surface models in order to estimate components of terrestrial water and energy balance. Precipitation forcing uncertainty is the principal source of spread among the ensembles that is required for utilizing information in observations to correct model priors. Precipitation fields may have both position and magnitude errors. However, current uncertainty characterizations of precipitation forcing in land data assimilation systems often do no more than applying multiplicative errors to precipitation fields. In this paper, an ensemble-based Bayesian method for characterization of uncertainties associated with precipitation retrievals from spaceborne instruments is introduced. This method is used to produce stochastic replicates of precipitation fields that are conditioned on precipitation observations. Unlike previous studies, the error likelihood is derived using an archive of historical measurements. The ensemble replicates are generated using a stochastic method, and they are intermittent in space and time. The replicates are first projected in a low-dimension subspace using a problem-specific set of attributes. The attributes are derived using a dimensionality reduction scheme that takes advantage of singular value decomposition. A nonparametric importance sampling technique is formulated in terms of the attribute vectors to solve the Bayesian sampling problem. Examples are presented using retrievals from operational passive microwave instruments, and performance of the method is assessed using ground validation measurements from a surface weather radar network. Results indicate that this ensemble characterization approach provides a useful description of precipitation uncertainties with a posterior ensemble that is narrower in distribution than its prior while containing both precipitation position and magnitude errors. [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 examines the synoptic control of precipitation contrasts across the Cascade Mountains of Washington State during the cool season. The average hourly precipitation for three stations on the western and eastern sides of the barrier is calculated, and composite synoptic conditions are determined for situations when precipitation is greater over the eastern slopes, similar precipitation falls on both sides, moderately greater precipitation falls on the western side, and substantially greater precipitation falls on the western side. Conditions prior to occluded/warm frontal passage are favorable for relatively equal precipitation amounts on both sides of the barrier, with extensive warm advection precipitation spreading over the region. Low-level winds are generally southerly during this period, resulting in less orographic modulation of precipitation on the north-south-oriented Cascades. In contrast, postfrontal conditions are associated with westerly flow, enhanced windward precipitation, and greater drying on the leeward (eastern) slopes. Postfrontal conditions also bring drying aloft and reduced stability at low levels, both of which contribute to the enhancement of precipitation on the western slopes. The variation of the cross-mountain precipitation ratio between El Niño and La Niña years can be explained by the associated synoptic configurations. El Niño years have weaker zonal winds and thus less orographic modulation. Furthermore, the lesser vertical stability of La Niña years enhances precipitation on the windward side of the barrier where shallow convection is released. [ABSTRACT FROM AUTHOR]

The South Asian monsoon is critically important for agricultural production in the region that includes the vast, fertile Indus and Ganges basins. However, the behavior of the South Asian monsoon is not well understood because of its complex nature, and existing instrumental climate records are insufficient for investigating the risks of the low-frequency but high-impact megadroughts that have historically occurred. This paper develops an inverse Palmer drought severity index (PDSI) model to retrieve paleoprecipitation for the region during the time period of 1300-1899, using available data of the water-holding capacity of soil, temperature, and reconstructed PDSI based on the tree-ring analysis of Cook et al.. Temperature data are reconstructed by a regression analysis utilizing an existing temperature reconstruction in an adjacent region and the Pacific decadal oscillation. Based on the retrieved paleoprecipitation, several megadroughts are identified during the reconstruction period. The drought frequency in the Indus basin is higher than that in the Ganges basin. The intensity, frequency, and spatial extent of severe droughts increased from 1300-1899 to 1900-2010. As a signal of climate change, increasing intensity and frequency of severe drought in the Indus and Ganges River basins needs adaptation strategies and drought preparedness measures to secure the food production in this area. [ABSTRACT FROM AUTHOR]

Although atmospheric rivers (ARs) typically weaken following landfall, those that penetrate inland can contribute to heavy precipitation and high-impact weather within the interior of western North America. In this paper, the authors examine the evolution of ARs over western North America using trajectories released at 950 and 700 hPa within cool-season ARs along the Pacific coast. These trajectories are classified as coastal decaying, inland penetrating, or interior penetrating based on whether they remain within an AR upon reaching selected transects over western North America. Interior-penetrating AR trajectories most frequently make landfall along the Oregon coast, but the greatest fraction of landfalling AR trajectories that eventually penetrate into the interior within an AR is found along the Baja Peninsula. In contrast, interior-penetrating AR trajectories rarely traverse the southern 'high' Sierra. At landfall, interior-penetrating AR trajectories are associated with a more amplified flow pattern, more southwesterly (vs westerly) flow along the Pacific coast, and larger water vapor transport ( qυ). The larger initial qυ of interior-penetrating AR trajectories is due primarily to larger initial water vapor q and wind speed υ for those initiated at 950 and 700 hPa, respectively. Inland- and interior-penetrating AR trajectories maintain large qυ over the interior partially due to increases in υ that offset decreases in q, particularly in the vicinity of topographical barriers. Therefore, synoptic conditions and trajectory pathways favoring larger initial qυ at the coast, limited water vapor depletion by orographic precipitation, and increases in υ over the interior are keys to differentiating interior-penetrating from coastal-decaying ARs. [ABSTRACT FROM AUTHOR]

The Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) measures reflectivity downward from space and provides observations of the vertical distributions of precipitation over land as well as the ocean. It overpasses the southern part of the Korean Peninsula where (i) a dense network of operational S-band scanning radars is available and (ii) various types of precipitation occur. By utilizing a 3D reflectivity composite from the ground S-band radar (GR) observations, this paper shows a comparison of reflectivity profiles observed with both PR and GR focusing on their vertical structure. For four cases of widespread rain, visual and statistical analyses show that PR attenuation-corrected reflectivity agrees closely with reflectivity observed from the GR composite below the melting layer. Above and within the melting layer, PR is affected critically by its sensitivity while GR beam broadening at far ranges causes systematic differences in the PR-GR comparisons. For four cases of convective rain, PR underestimates the mean reflectivities by 1-3 dB compared with those from GR at low levels where precipitation attenuation is significant toward the ground. In these cases, the low sensitivity of PR results in a small number of matched points for weak echoes. Also, the PR-GR discrepancy for the convective case is more affected by time mismatching. [ABSTRACT FROM AUTHOR]

This paper evaluates the seasonal (winter, premonsoon, monsoon, and postmonsoon) performance of seven precipitation products from three different sources: gridded station data, satellite-derived data, and reanalyses products over the Indian subcontinent for a period of 10 years (1997/98-2006/07). The evaluated precipitation products are the Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation of the Water Resources (APHRODITE), the Climate Prediction Center unified (CPC-uni), the Global Precipitation Climatology Project (GPCP), the Tropical Rainfall Measuring Mission (TRMM) post-real-time research products (3B42-V6 and 3B42-V7), the Climate Forecast System Reanalysis (CFSR), and the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim). Several verification measures are employed to assess the accuracy of the data. All datasets capture the large-scale characteristics of the seasonal mean precipitation distribution, albeit with pronounced seasonal and/or regional differences. Compared to APHRODITE, the gauge-only (CPC-uni) and the satellite-derived precipitation products (GPCP, 3B42-V6, and 3B42-V7) capture the summer monsoon rainfall variability better than CFSR and ERA-Interim. Similar conclusions are drawn for the postmonsoon season, with the exception of 3B42-V7, which underestimates postmonsoon precipitation. Over mountainous regions, 3B42-V7 shows an appreciable improvement over 3B42-V6 and other gauge-based precipitation products. Significantly large biases/errors occur during the winter months, which are likely related to the uncertainty in observations that artificially inflate the existing error in reanalyses and satellite retrievals. [ABSTRACT FROM AUTHOR]

This paper documents the characteristics of extreme precipitation events (EPEs) in the southeastern United States (SEUS) during 2002-11. The EPEs are identified by applying an object-based method to 24-h precipitation analyses from the NCEP stage-IV dataset. It is found that EPEs affected the SEUS in all months and occurred most frequently in the western portion of the SEUS during the cool season and in the eastern portion during the warm season. The EPEs associated with tropical cyclones, although less common, tended to be larger in size, more intense, and longer lived than 'nontropical' EPEs. Nontropical EPEs in the warm season, relative to those in the cool season, tended to be smaller in size and typically involved more moist, conditionally unstable conditions but weaker dynamical influences. Synoptic-scale composites are constructed for nontropical EPEs stratified by the magnitude of vertically integrated water vapor transport (IVT) to examine distinct scenarios for the occurrence of EPEs. The composite results indicate that 'strong IVT' EPEs occur within high-amplitude flow patterns involving strong transport of moist, conditionally unstable air within the warm sector of a cyclone, whereas 'weak IVT' EPEs occur within low-amplitude flow patterns featuring weak transport but very moist and conditionally unstable conditions. Finally, verification of deterministic precipitation forecasts from a reforecast dataset based on the NCEP Global Ensemble Forecast System reveals that weak-IVT EPEs were characteristically associated with lower forecast skill than strong-IVT EPEs. Based on these results, it is suggested that further research should be conducted to investigate the forecast challenges associated with EPEs in the SEUS. [ABSTRACT FROM AUTHOR]