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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]

In this paper, the reliability of the wind speed, temperature, humidity, pressure, and precipitation values of three surface meteorological forcing products [China Meteorological Administration Land Data Assimilation System, version 2 (CLDAS-2); China Meteorological Forcing Dataset (CMFD); and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2)] in the Tibetan Plateau (TP) region was investigated from 2008 to 2014. Compared with the China Meteorological Administration (CMA) observations, CLDAS-2 exhibited the highest correlation coefficient for wind speed, CMFD displayed the best coefficients for temperature and specific humidity, and MERRA-2 best reflected pressure variations. Based on the biases, CLDAS-2 exhibited the best overall performance for temperature, specific humidity, and pressure, while CMFD displayed the best performance for wind speed. The high overall accuracy and false alarm ratio of precipitation based on MERRA-2 both stem from its continuous overestimation of the precipitation frequency. Both CLDAS-2 and CMFD overestimated the nonprecipitation frequency in comparisons with CMA observations, and a significant positive bias exists in MERRA-2 based on the analysis of daily precipitation. The results obtained from the comparisons with field observations over the TP and CMA observations are similar, except for the temperature and humidity biases of CLDAS-2. The meteorological effects on the coupled land-blowing snow modeling discussed in this paper suggest that the occurrence of blowing snow and snowdrift sublimation are projected to be reduced by CLDAS-2 due to the underestimation of wind speed, continual lack of snowfall events, and the positive biases in low temperatures and humidity, while simulations of blowing processes by MERRA-2 are likely to be much more severe than they actually are. These results may contribute to identifying deficiencies associated with the development of land surface models coupled with a blowing snow model. [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]

This paper describes a comprehensive assessment of a new high-resolution, gauge–satellite-based analysis of daily precipitation over continental South America during 2004. This methodology is based on a combination of additive and multiplicative bias correction schemes to get the lowest bias when compared with the observed values (rain gauges). Intercomparisons and cross-validation tests have been carried out between independent rain gauges and different merging techniques. This validation process was done for the control algorithm [Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis real-time algorithm] and five different merging schemes: additive bias correction; ratio bias correction; TRMM Multisatellite Precipitation Analysis, research version; and the combined scheme proposed in this paper. These methodologies were tested for different months belonging to different seasons and for different network densities. All compared, merging schemes produce better results than the control algorithm; however, when finer temporal (daily) and spatial scale (regional networks) gauge datasets are included in the analysis, the improvement is remarkable. The combined scheme consistently presents the best performance among the five techniques tested in this paper. This is also true when a degraded daily gauge network is used instead of a full dataset. This technique appears to be a suitable tool to produce real-time, high-resolution, gauge- and satellite-based analyses of daily precipitation over land in regional domains. [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]

In this paper, we examined the spatial and temporal variations in precipitation amount, frequency, and intensity in China based on daily precipitation data from 2050 weather stations from 1973 to 2016. We used two Markov chain parameters to quantify the wet persistence and dry persistence that characterizes the temporal pattern of wet and dry days, respectively. We found that China's annual precipitation changed little from 1973 to 2016, but varied dramatically from 524 to 688mm yr-1, with an average of 592 mm yr-1, during this period. China's precipitation frequency, the number of days with effective precipitation (.0.1mmday21) in a year, significantly decreased at a rate of 0.9 days decade21 from 1973 to 2016, but precipitation intensity significantly increased at a rate of 0.12 mm day-1 decade-1 during the same period. Of the changes in China's total precipitation amount, precipitation intensity played a dominant role, contributing 70.8%, while precipitation frequency contributed the remaining 29.2%. Little change was found in the wet persistence in China over the period of 1973-2016, but the dry persistence significantly increased with an average increasing trend of 1.62 3 1023 probability per decade during the same period, and no significant correlations were found between these two variables. China's precipitation also changed nonuniformly in space, with increasing trends in precipitation amount, frequency, intensity, and wet persistence in western and northeastern China but decreasing trends in the Sichuan basin, northeast of Inner Mongolia, and the Beijing-Tianjin-Hebei region. [ABSTRACT FROM AUTHOR]

The lack of understanding on the soil moisture-precipitation feedback mechanisms remains a large source of uncertainty for land-atmosphere coupled models. Previous observation-based studies on the soil moisture-precipitation feedback in Illinois have shown contradictory results. This paper extends earlier research by providing a more holistic analysis considering different scales based on an 11-yr (2003-13) hourly soil moisture dataset, which makes it possible to revisit the disputed hypothesis on the correlation between warm-season soil moisture and subsequent precipitation. This study finds a strong positive correlation between late spring/early summer state-average soil moisture at the root-zone depths and subsequent state-average summer precipitation. On the daily to weekly time scale, however, no relation is found. Moreover, regional analysis suggests that precipitation variability over central Illinois can be best explained by the soil moisture variability in northwest Illinois. Using a back-trajectory method [Water Accounting Model-2 layers (WAM-2layers)] from May to July, the evaporative sources of precipitation in Illinois are identified. The pattern of the source regions shows little interannual variability, while the strength of the sources changes significantly and the Gulf of Mexico contributes more during wet years. However, strong influences (teleconnections) of sea surface temperatures on the subsequent precipitation variability in Illinois are not found on a seasonal scale. The long time scale of the soil moisture-precipitation correlation and the weak influences of SSTs and climate indices may suggest that precipitation variability in spring/summer in Illinois is mostly related to continental-scale soil moisture-precipitation feedback. [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]

Satellite-retrieved precipitation has the potential to support flood modeling in mountainous areas. However, to reach this potential satellite estimates need to be corrected for the severe underestimation exhibited in orography-induced heavy precipitation events (HPEs). This paper assesses an existing satellite precipitation error correction technique driven by high-resolution numerical weather prediction (NWP) simulations of HPEs in complex terrain. The study is based on NOAA Climate Prediction Center morphing technique (CMORPH) high-resolution precipitation estimates of six such events induced by hurricane landfalls in the southern Appalachian mountainous region. A distributed hydrological model (Coupled Routing and Excess Storage model) is applied to evaluate the impact of the proposed satellite precipitation error correction on flood simulations for 20 basins of various sizes in this mountainous region. The results demonstrate significant improvements due to the NWP-based adjustment technique in terms of both the precipitation error characteristics and corresponding runoff simulations. These improvements are shown to be comparable to those from the postprocessed gauge-adjusted CMORPH precipitation product, which is promising for advancing hydrologic uses of satellite rainfall in mountainous areas lacking ground observations. [ABSTRACT FROM AUTHOR]

Despite the advantage of global coverage at high spatiotemporal resolutions, satellite remotely sensed precipitation estimates still suffer from insufficient accuracy that needs to be improved for weather, climate, and hydrologic applications. This paper presents a framework of a deep neural network (DNN) that improves the accuracy of satellite precipitation products, focusing on reducing the bias and false alarms. The state-of-the-art deep learning techniques developed in the area of machine learning specialize in extracting structural information from a massive amount of image data, which fits nicely into the task of retrieving precipitation data from satellite cloud images. Stacked denoising autoencoder (SDAE), a widely used DNN, is applied to perform bias correction of satellite precipitation products. A case study is conducted on the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) with spatial resolution of 0.08° × 0.08° over the central United States, where SDAE is used to process satellite cloud imagery to extract information over a window of 15 × 15 pixels. In the study, the summer of 2012 (June-August) and the winter of 2012/13 (December-February) serve as the training periods, while the same seasons of the following year (summer of 2013 and winter of 2013/14) are used for validation purposes. To demonstrate the effectiveness of the methodology outside the study area, three more regions are selected for additional validation. Significant improvements are achieved in both rain/no-rain ( R/NR) detection and precipitation rate quantification: the results make 33% and 43% corrections on false alarm pixels and 98% and 78% bias reductions in precipitation rates over the validation periods of the summer and winter seasons, respectively. [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 karst terrains, fractures and conduits often occur in clusters, forming groundwater quick flow belts, which are the major passages of groundwater and solute transport. We propose a cost-effective method that utilizes precipitation and spring discharge data to identify groundwater quick flow belts by the multitaper method (MTM). In this paper, hydrological processes were regarded as the transformation of precipitation signals to spring discharge signals in a karst spring catchment. During the processes, karst aquifers played the role of signal filters. Only those signals with high energy could penetrate through aquifers and reflect in the spring discharge, while other weak signals were filtered out or altered by aquifers. Hence, MTM was applied to detect and reconstruct the signals that penetrate through aquifers. Subsequently, by analyzing the reconstructed signals of precipitation with those of spring discharge, we acquired the hydraulic response time and identified the quick flow belts. Finally, the methods were applied to the Niangziguan Spring (NS) catchment, China. Results showed that the hydraulic response time of the spring discharge to precipitation was 3 months at Pingding County; 4 months at Yuxian County, Yangquan City, Xiyang County, and Heshun County; and 27 months at Shouyang County and Zouquan County. These results suggested that Pingding County is located at a groundwater quick flow belt, which is a major passage of groundwater and contaminants, in the NS catchment. This is important since Pingding County is not only the key development area of karst groundwater but also the key conservation area for sustainable development of karst groundwater resources in NS catchment. [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]

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]

Extreme precipitation associated with extratropical cyclones can lead to flooding if cyclones track over land. However, the dynamical mechanisms by which moist air is transported into cyclones is poorly understood. In this paper we analyze airflows within a climatology of cyclones in order to understand how cyclones redistribute moisture stored in the atmosphere. This analysis shows that within a cyclone's warm sector the cyclone-relative airflow is rearwards relative to the cyclone propagation direction. This low-level airflow (termed the feeder airstream) slows down when it reaches the cold front, resulting in moisture flux convergence and the formation of a band of high moisture content. One branch of the feeder airstream turns toward the cyclone center, supplying moisture to the base of the warm conveyor belt where it ascends and precipitation forms. The other branch turns away from the cyclone center exporting moisture from the cyclone. As the cyclone travels, this export results in a filament of high moisture content marking the track of the cyclone (often used to identify atmospheric rivers). We find that both cyclone precipitation and water vapor transport increase when moisture in the feeder airstream increases, thus explaining the link between atmospheric rivers and the precipitation associated with warm conveyor belt ascent. Atmospheric moisture budgets calculated as cyclones pass over fixed domains relative to the cyclone tracks show that continuous evaporation of moisture in the precyclone environment moistens the feeder airstream. Evaporation behind the cold front acts to moisten the atmosphere in the wake of the cyclone passage, potentially preconditioning the environment for subsequent cyclone development. [ABSTRACT FROM AUTHOR]

The OLYMPEX field campaign, which took place around the Olympic Mountains of Washington State during winter 2015/16, provided data for evaluating the simulated microphysics and precipitation over and near that barrier. Using OLYMPEX observations, this paper assesses precipitation and associated microphysics in the WRF-ARW model over the U.S. Pacific Northwest. Model precipitation from the University of Washington real-time WRF forecast system during the OLYMPEX field program (November 2015–February 2016) and an extended period (2008–18) showed persistent underprediction of precipitation, reaching 100 mm yr−1 over the windward side of the coastal terrain. Increasing horizontal resolution does not substantially reduce this underprediction. Evaluating surface disdrometer observations during the 2015/16 OLYMPEX winter, it was found that the operational University of Washington WRF modeling system using Thompson microphysics poorly simulated the rain drop size distribution over a windward coastal valley. Although liquid water content was represented realistically, drop diameters were overpredicted, and, consequently, the rain drop distribution intercept parameter was underpredicted. During two heavy precipitation periods, WRF realistically simulated environmental conditions, including wind speed, thermodynamic structures, integrated moisture transport, and melting levels. Several microphysical parameterization schemes were tested in addition to the Thompson scheme, with each exhibiting similar biases for these two events. We show that the parameterization of aerosols over the coastal Northwest offered only minor improvement. [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]

Extreme precipitation can have significant adverse impacts on infrastructure and property, human health, and local economies. This paper examines recent changes in extreme precipitation in the northeast United States. Daily station data from 58 stations missing less than 5% of days for the years 1979–2014 from the U.S. Historical Climatology Network were used to analyze extreme precipitation, defined as the top 1% of days with precipitation. A statistically significant (95% confidence level) increasing trend of the threshold for the top 1% of extreme precipitation events was found (0.3 mm yr−1). This increasing trend was due to both an increase in the frequency of extreme events and the magnitude of extreme events. Rainfall events ≥ 150 mm (24-h accumulation) increased in frequency from 6 events between 1979 and 1996 to 25 events between 1997 and 2014, a 317% increase. The annual daily maximum precipitation, or the highest recorded precipitation amount in a given year, increased by an average of 1.6 mm yr−1, a total increase of 58.0 mm. Decreasing trends in extreme precipitation were observed east of Lake Erie during the warm season. Increasing trends in extreme precipitation were most robust during the fall months of September, October, and November, and particularly at locations further inland. The analysis showed that increases in events that were tropical in nature, or associated with tropical moisture, led to the observed increase in extreme precipitation during the fall months. [ABSTRACT FROM AUTHOR]

This paper evaluates nine satellite rainfall products and the Global Precipitation Centre Climatology (GPCC) gauge dataset over the Congo basin. For the evaluation the reference dataset is a newly created, gridded gauge dataset based on a gauge network that is more complete than that of GPCC in recent years. It is termed NIC131-gridded. Gridding was achieved via a climatic reconstruction method based on principal components, so that reliable estimates of rainfall are available even in the data-sparse central basin. The satellite products were evaluated for two locations, the Congo basin and areas on its eastern and western periphery (termed the "east plus west" sector). The station density was notably higher in the latter region. Two time periods were also considered: 1983–94, when station density was relatively high, and 1998–2010, when station density was much lower than during the earlier period. Several products show excellent agreement with the NIC131-gridded reference dataset. These include CHIRPS2, PERSIANN-CDR, GPCP 2.3, TRMM 3B43, and, to a lesser extent, GPCC V7. RMSE for the period 1983–94 in the east plus west sector is on the order of 20 mm month−1 for GPCC V7 and 20–30 mm month−1 for the other products. The compares with 40–60 mm month−1 for the most poorly performing products, African Rainfall Climatology version 2 (ARCv2) and CMAP. Over the Congo basin, RMSE for those two products is about the same as in the east plus west sector but is on the order of 30–40 mm month−1 for the better-performing products. In all cases, the performance of the 10 products evaluated is notably poorer in recent years (1998–2010), when the station network is sparse, than during the period 1983–94, when the dense station network provides reliable estimates of rainfall. For the more recent period RMSE is on the order of 30–40 mm month−1 for the best-performing products in the east plus west sector but only slightly higher over the Congo basin. All products do reasonably well in reproducing the seasonal cycle and the latitudinal gradients of rainfall. Estimates of interannual variability show more scatter among the various products and are less reliable. Overall, the most important results of the study are to demonstrate the strong impact that actual gauge data have on the various products and the need to have access to such gauge data, in order to produce reliable rainfall estimates from satellites. [ABSTRACT FROM AUTHOR]

In the past few decades, there have been more extreme climate events occurring worldwide, including Canada, which has also suffered from many extreme precipitation events. In this paper, trend analysis, probability distribution functions, principal component analysis, and wavelet analysis were used to investigate the spatial and temporal patterns of extreme precipitation events of Canada. Ten extreme precipitation indices were calculated using long-term daily precipitation data (1950–2012) from 164 Canadian gauging stations. Several large-scale climate patterns such as El Niño–Southern Oscillation (ENSO), Pacific decadal oscillation (PDO), Pacific–North American (PNA), and North Atlantic Oscillation (NAO) were selected to analyze the relationships between extreme precipitation and climate indices. Convective available potential energy (CAPE), specific humidity, and surface temperature were employed to investigate potential causes of trends in extreme precipitation. The results reveal statistically significant positive trends for most extreme precipitation indices, which means that extreme precipitation of Canada has generally become more severe since the mid-twentieth century. The majority of indices display more increasing trends along the southern border of Canada while decreasing trends dominated the central Canadian Prairies. In addition, strong teleconnections are found between extreme precipitation and climate indices, but the effects of climate patterns differ from region to region. Furthermore, complex interactions of climate patterns with synoptic atmospheric circulations can also affect precipitation variability, and changes to the summer and winter extreme precipitation could be explained more by the thermodynamic impact and the combined thermodynamic and dynamic effects, respectively. The seasonal CAPE, specific humidity, and temperature are correlated to Canadian extreme precipitation, but the correlations are season dependent, which could be positive or negative. [ABSTRACT FROM AUTHOR]

Monitoring changes of precipitation phase from space is important for understanding the mass balance of Earth's cryosphere in a changing climate. This paper examines a Bayesian nearest neighbor approach for prognostic detection of precipitation and its phase using passive microwave observations from the Global Precipitation Measurement (GPM) satellite. The method uses the weighted Euclidean distance metric to search through an a priori database populated with coincident GPM radiometer and radar observations as well as ancillary snow-cover data. The algorithm performance is evaluated using data from GPM official precipitation products, ground-based radars, and high-fidelity simulations from the Weather Research and Forecasting Model. Using the presented approach, we demonstrate that the hit probability of terrestrial precipitation detection can reach to 0.80, while the probability of false alarm remains below 0.11. The algorithm demonstrates higher skill in detecting snowfall than rainfall, on average by 10%. In particular, the probability of precipitation detection and its solid phase increases by 11% and 8%, over dry snow cover, when compared to other surface types. The main reason is found to be related to the ability of the algorithm in capturing the signal of increased liquid water content in snowy clouds over radiometrically cold snow-covered surfaces. [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]

Green vegetation fraction (GVF) plays a crucial role in the atmosphere–land water and energy exchanges. It is one of the essential parameters in the Noah land surface model (LSM) that serves as the land component of a number of operational numerical weather prediction models at the National Centers for Environmental Prediction (NCEP) of NOAA. The satellite GVF products used in NCEP models are derived from a simple linear conversion of either the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) currently or the enhanced vegetation index (EVI) from the Visible Infrared Imaging Radiometer Suite (VIIRS) planned for the near future. Since the NDVI or EVI is a simple spectral index of vegetation cover, GVFs derived from them may lack the biophysical meaning required in the Noah LSM. Moreover, the NDVI- or EVI-based GVF data products may be systematically biased over densely vegetated regions resulting from the saturation issue associated with spectral vegetation indices. On the other hand, the GVF is physically related to the leaf area index (LAI), and thus it could be beneficial to derive GVF from LAI data products. In this paper, the EVI-based and the LAI-based GVF derivation methods are mathematically analyzed and are found to be significantly different from each other. Impacts of GVF differences on the Noah LSM simulations and on weather forecasts of the Weather Research and Forecasting (WRF) Model are further assessed. Results indicate that LAI-based GVF outperforms the EVI-based one when used in both the offline Noah LSM and WRF Model. [ABSTRACT FROM AUTHOR]

This study addresses in general terms the problem of the optimal combination of multiple observation datasets. Only satellite-retrieved geophysical parameter datasets are considered here (not the raw satellite observations). This study focuses on the terrestrial water cycle and presents methodologies to obtain a coherent dataset of four water cycle key components: precipitation, evapotranspiration, runoff, and terrestrial water storage. Various innovative 'integration' methodologies are introduced: simple weighting (SW), constrained linear (CL), optimal interpolation (OI), and neural networks (NN). The term 'integration' will be used here, not 'assimilation,' as no model will be included in the data fusion process. A simple postprocessing filtering (PF) step can be used to impose the water cycle budget closure after the integration method. It is shown that this constraint actually improves the estimation of the water cycle components. The integration techniques are tested using real observation data over the Mississippi and Niger basins from satellite and in situ measurements. A Monte Carlo experiment with a synthetic uncertainty perturbation model is used to measure the ability of the SW, OI, and NN, with or without the PF step, to retrieve the four water cycle components. Once the PF closure constraint is added, the methodologies have equivalent accuracies. The need for these types of methodologies should increase in the future since multiple observation datasets are now available and the climate community needs to combine them into a unique, optimal, and coherent dataset of multiple parameters. A companion paper will test these methodologies on satellite observation datasets at the basin and global scales. [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]

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]

This paper studies the atmospheric moisture residence times over China for the period 1980–2009 using the dynamic recycling model (DRM). We define both the residence times for atmospheric moisture of precipitation (backward tracking) and evaporation (forward tracking) and show that each has significant spatial and seasonal variations. The area-averaged precipitation-moisture residence time is approximately 8.3 days, while the evaporation residence time is approximately 6.3 days. In addition, we investigate the concept of “tracking time” or time selected for moisture tracking in numerical source–sink studies. The area-averaged backward and forward tracking times at the 90% threshold (i.e., when 90% of initial moisture is attributed for tracking) are approximately 22 and 15 days, respectively. Finally, we theoretically deduced the explicit expressions for residence and tracking times for idealized cases and found the analytical proportional relationship between these times. In this way, the analytical link between residence time and e-folding time was reestablished. This proportional relationship was further verified against the DRM-derived values. In the DRM results, the proportional relation generally fluctuates along the trajectory, which leads to the differences between the theoretical and the DRM-derived values. These results can enhance our understanding of water cycling, and they are likely to help choose tracking times in relevant studies. [ABSTRACT FROM AUTHOR]

Natural weather systems possess certain spatiotemporal variability and correlations. Preserving these spatiotemporal properties is a significant challenge in postprocessing ensemble weather forecasts. To address this challenge, several rank-based methods, the Schaake Shuffle and its variants, have been developed in recent years. This paper presents an extensive assessment of the Schaake Shuffle and its two variants. These schemes differ in how the reference multivariate rank structure is established. The first scheme (SS-CLM), an implementation of the original Schaake Shuffle method, relies on climatological observations to construct rank structures. The second scheme (SS-ANA) utilizes precipitation event analogs obtained from a historical archive of observations. The third scheme (SS-ENS) employs ensemble members from the Global Ensemble Forecast System (GEFS). Each of the three schemes is applied to postprocess precipitation ensemble forecasts from the GEFS for its first three forecast days over the mid-Atlantic region of the United States. In general, the effectiveness of these schemes depends on several factors, including the season (or precipitation pattern) and the level of gridcell aggregation. It is found that 1) the SS-CLM and SS-ANA behave similarly in spatial and temporal correlations; 2) by a measure for capturing spatial variability, the SS-ENS outperforms the SS-ANA, which in turn outperforms the SS-CLM; and 3), overall, the SS-ANA performs better than the SS-CLM. The study also reveals that it is important to choose a proper size for the postprocessed ensembles in order to capture extreme precipitation events. [ABSTRACT FROM AUTHOR]

The constellation of spaceborne passive microwave (MW) sensors, coordinated under the framework of the Precipitation Measurement Missions international agreement, continuously produces observations of clouds and precipitation all over the globe. The Goddard profiling algorithm (GPROF) is designed to infer the instantaneous surface precipitation rate from the measured MW radiances. The last version of the algorithm (GPROF-2014)— the product of more than 20 years of algorithmic development, validation, and improvement—is currently used to estimate precipitation rates from the microwave imager GMI on board the GPM core satellite. The previous version of the algorithm (GPROF-2010) was used with the microwave imager TMI on board TRMM. In this paper, TMI-GPROF-2010 estimates and GMI-GPROF-2014 estimates are compared with coincident active measurements from the Precipitation Radar on board TRMM and the Dual-Frequency Precipitation Radar on board GPM, considered as reference products. The objective is to assess the improvement of the GPM-era microwave estimates relative to the TRMM-era estimates and diagnose regions where continuous improvement is needed. The assessment is oriented toward estimating the "effective resolution" of the MW estimates, that is, the finest scale at which the retrieval is able to accurately reproduce the spatial variability of precipitation. A wavelet-based multiscale decomposition of the radar and passive microwave precipitation fields is used to formally define and assess the effective resolution. It is found that the GPM-era MW retrieval can resolve finer-scale spatial variability over oceans than the TRMM-era retrieval. Over land, significant challenges exist, and this analysis provides useful diagnostics and a benchmark against which future retrieval algorithm improvement can be assessed. [ABSTRACT FROM AUTHOR]

The May 2015 record rainfall that occurred across Oklahoma was the result of a large number of high-intensity rain events. A unique set of observations from gauges in the Oklahoma Mesonet, the NWS Cooperative Observer (COOP) network, the Community Collaborative Rain, Hail and Snow (CoCoRaHS) network, an experimental pit gauge system, and NWS radar was available that covered an area in and around Norman, Oklahoma. This paper documents the performance of the various gauges throughout the course of the month. Key findings are 1) observations from all methods significantly exceeded the 200-yr return interval; 2) a weighing-bucket gauge at ground level recorded amounts up to 4.5% higher than a similarly located ground-level tipping-bucket gauge and up to 8.2% higher than a nearby aboveground tipping-bucket gauge; 3) a manual COOP gauge recorded nearly identical (within 1.2%) observations as compared to an automated tipping-bucket gauge at a collocated Mesonet station; and 4) observations from 26 CoCoRaHS gauges yielded an average rainfall within 1% of the aerially averaged radar rainfall derived from the Multisensor Precipitation Estimator. [ABSTRACT FROM AUTHOR]

An automatic device for measurement of the amount (weight) of deposited precipitation developed at the Institute of Atmospheric Physics, Academy of Sciences of the Czech Republic, is described. Examples of measurements of various types of deposited precipitation are presented. The paper also discusses the response of the measuring instrument to falling precipitation and the influence of wind on the measurements. The results of first measurements proved that the instrument is suitable for automatic and continuous monitoring of deposited precipitation. [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]

Recently, a single-site stochastic precipitation model called ''the mixture of generalized chain-dependent processes conditioned on a climate variable'' was developed. The model can effectively eliminate overdispersion--that is, underestimation in variance of seasonal precipitation total. In this paper, the single-site model is further developed into a multisite stochastic precipitation model by driving a collection of individual single-site models, but with spatial dependence following a method proposed by D. S. Wilks. Specifically, a computationally effective algorithm for estimating the spatial dependence of precipitation occurrence is developed to replace the construction of the empirical curves in the Wilks method. An effective and straightforward approach for correcting the bias of the spatial correlation of precipitation intensity is also proposed. This model is tested on a small network of sites from a significant hydroelectric power generation region of South Island, New Zealand. [ABSTRACT FROM AUTHOR]

The Lagrangian Model (LMODEL) is a new multisensor satellite rainfall monitoring methodology based on the use of a conceptual cloud-development model that is driven by geostationary satellite imagery and is locally updated using microwave-based rainfall measurements from low earth-orbiting platforms. This paper describes the cloud development model and updating procedures; the companion paper presents model validation results. The model uses single-band thermal infrared geostationary satellite imagery to characterize cloud motion, growth, and dispersal at high spatial resolution (∼4 km). These inputs drive a simple, linear, semi-Lagrangian, conceptual cloud mass balance model, incorporating separate representations of convective and stratiform processes. The model is locally updated against microwave satellite data using a two-stage process that scales precipitable water fluxes into the model and then updates model states using a Kalman filter. Model calibration and updating employ an empirical rainfall collocation methodology designed to compensate for the effects of measurement time difference, geolocation error, cloud parallax, and rainfall shear. [ABSTRACT FROM AUTHOR]

The objective of this paper is to improve the performance of a hydrologic model through the assimilation of observed discharge. Since an observation of discharge at a certain time is always influenced by the catchment wetness conditions and meteorology in the past, the assimilation method will have to modify both the past and present soil wetness conditions. For this purpose, a bias-corrected retrospective ensemble Kalman filter has been used as the assimilation algorithm. The assimilation methodology takes into account bias in the forecast state variables for the calculation of the optimal estimates. A set of twin experiments has been developed, in which it is attempted to correct the model results obtained with erroneous initial conditions and strongly over- and underestimated precipitation data. The results suggest that the assimilation of observed discharge can correct for erroneous model initial conditions. When the precipitation used to force the model is underestimated, the assimilation of observed discharge can reduce the bias in the modeled turbulent fluxes by approximately 50%. This is due to a correction of the modeled soil moisture. In the case of an overestimation of the precipitation, an improvement in the modeled wetness conditions is also obtained after data assimilation, but this does not lead to a significant improvement in the modeled energy balance. The results in this paper indicate that there is potential to improve the estimation of hydrologic states and fluxes through the assimilation of observed discharge data. [ABSTRACT FROM AUTHOR]

This paper examines the sensitivity of regional climate simulations to increasing spatial resolution via nesting by means of a 20-yr simulation of the western United States at 40-km resolution and a 5-yr simulation at 13-km resolution for the Pacific Northwest and California. The regional simulation at 40-km resolution shows a lack of precipitation along coastal hills, good agreement with observations on the windward slopes of the Cascades and Sierra Nevada, but overprediction on the leeside and the basins beyond. Snowpack is grossly underpredicted throughout the western United States when compared against snowpack telemetry (snotel) observations. During winter, higher spatial resolution mainly improves the precipitation simulation in the coastal hills and basins. Along the Cascades and the Sierra Nevada range, precipitation is strongly amplified at the higher spatial resolution. Higher resolution generally improves the spatial distribution of precipitation to yield a higher spatial correlation between simulations and observations. During summer, higher resolution improves not only the spatial distribution but also the regional mean precipitation. In the Olympic Mountains and along the Coastal Range, increased precipitation at higher resolution reflects mainly a shift from light to heavy precipitation events. In the Cascades and Sierra Nevada, increased precipitation is mainly associated with more frequent heavy precipitation at higher resolution. Changes in precipitation from 40- to 13-km resolution depend on synoptic conditions such as wind direction and moisture transport. The use of higher spatial resolution improves snowpack more than precipitation. However, results presented in this paper suggest that accuracy in the snow simulation is also limited by factors such as deficiencies in the land surface model or biases in other model variables. [ABSTRACT FROM AUTHOR]

The stochastic analysis, modeling, and simulation of climatic and hydrologic processes such as precipitation, streamflow, and sea surface temperature have usually been based on assumed stationarity or randomness of the process under consideration. However, empirical evidence of many hydroclimatic data shows temporal variability involving trends, oscillatory behavior, and sudden shifts. While many studies have been made for detecting and testing the statistical significance of these special characteristics, the probabilistic framework for modeling the temporal dynamics of such processes appears to be lacking. In this paper a family of stochastic models that can be used to capture the dynamics of abrupt shifts in hydroclimatic time series is proposed. The applicability of such “shifting mean models” are illustrated by using time series data of annual Pacific decadal oscillation (PDO) indices and annual streamflows of the Niger River. [ABSTRACT FROM AUTHOR]

This paper describes the initial work toward the production of monthly global (land and ocean) analyses of precipitation for an extended period from 1948 to the present. Called the precipitation reconstruction (PREC), the global analyses are defined by interpolation of gauge observations over land (PREC/L) and by EOF reconstruction of historical observations over ocean (PREC/O). This paper documents the creation of the land component of the analyses (PREC/L) on a 2.5° latitude/longitude grid for 1948–2000. These analyses are derived from gauge observations from over 17 000 stations collected in the Global Historical Climatology Network (GHCN), version 2, and the Climate Anomaly Monitoring System (CAMS) datasets. To determine the most suitable objective analysis procedure for gridding, the analyses generated by four published objective analysis techniques [those of Cressman, Barnes, and Shepard, and the optimal interpolation (OI) method of Gandin] were compared. The evaluation demonstrated two crucial points: 1) better results are obtained when interpolating anomalies rather than the precipitation totals, and 2) the OI analysis procedure provided the most accurate and stable analyses among the four algorithms that were tested. Based on these results, the OI technique was used to create monthly gridded analyses of precipitation over the global land areas for the 53-yr period from 1948 to 2000. In addition, some diagnostic investigations of the seasonal and interannual variability of large-scale precipitation over the global land areas are presented. The mean distribution and annual cycle of precipitation observed in the PREC/L showed good agreement with those in several published gauge-based datasets, and the anomaly patterns associated with ENSO resemble those found in previous studies. The gauge-based dataset (PREC/L) will be updated on a quasi-real-time basis and is available online (). [ABSTRACT FROM AUTHOR]

Several dynamically downscaled climate simulations with various spatial resolutions (24, 12, and 4 km) and spectral nudging strengths (0, 600, and 2000 km) have been run over the contiguous United States from 2000 to 2009 using the high-resolution NASA Unified Weather and Research Forecasting (NU-WRF) regional model initialized and constrained by the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). This paper summarizes the authors' efforts on the development of a model performance metric and its application to assess summer precipitation over the U.S. Great Plains (USGP) in these downscaled climate simulations. A new model performance metric T was first developed that uses both the linear correlation coefficient and mean square error and is consistent with other commonly used metrics, but gives a bigger separation between good and bad simulations. This metric T was then applied to the summer mean precipitation spatial pattern, diurnal Hovmöller diagram, and diurnal spatial pattern over the USGP from the simulations focusing on the summer precipitation diurnal cycle related to mesoscale convective systems (MCSs). The metric T skill scores increase significantly from the control simulation to the nudged simulations and from the nudged simulations with shorter wavelengths to the nudged simulations with longer wavelengths, but do not change much from MERRA-2 to the downscaled simulations or between the various downscaled simulations with different spatial resolutions. Thus, there is some credibility, but no significant value added compared to MERRA-2, of the downscaled climate simulations of the summer precipitation over the USGP. [ABSTRACT FROM AUTHOR]

The U.S. Weather Surveillance Radar-1988 Doppler (WSR-88D) network has provided meteorologists and hydrologists with quantitative precipitation observations at an unprecedented high spatial-temporal resolution since its deployment in the mid-1990s. Since each single radar can only cover a maximum range of 460 km, a mosaic of multiple-radar observations is needed to generate any national-scale products. The Multi-Radar Multi-Sensor (MRMS) system utilizes a physically based two-dimensional mosaicking algorithm of the WSR-88D data to generate seamless national quantitative precipitation estimation (QPE) products. For areas covered by multiple radars, the mosaicking scheme first determines if precipitation is present by checking the lowest-altitude observation. If the lowest observed radar data indicate no precipitation, then the mosaicked value is set to no precipitation. Otherwise, a weighted mean of multiple-radar observations is taken as the mosaicked value. The weighting function is based on multiple factors, including the distance from the radar and the height of the observation with respect to the melting layer. The mosaic algorithm uses the physically lowest radar observations with no/little blockage while maintaining a spatial continuity in the mosaicked field. The performance of the MRMS seamless radar mosaic algorithm was examined for various precipitation events of different characteristics. The results of these case evaluations are presented in this paper. [ABSTRACT FROM AUTHOR]

This paper uses 620 station years of hourly Canadian Prairie climate data to analyze the coupling of monthly near-surface climate with opaque cloud, a surrogate for radiation, and precipitation anomalies. While the cloud-climate coupling is strong, precipitation anomalies impact monthly climate for as long as 5 months. The April climate has memory of precipitation anomalies back to freeze-up in November, mostly stored in the snowpack. The summer climate has memory of precipitation anomalies back to the beginning of snowmelt in March. In the warm season, mean temperature is strongly correlated to opaque cloud anomalies, but only weakly to precipitation anomalies. Mixing ratio anomalies are correlated to precipitation, but only weakly to cloud. The diurnal cycle of mixing ratio shifts upward with increasing precipitation anomalies. Positive precipitation anomalies are coupled to a lower afternoon lifting condensation level and a higher afternoon equivalent potential temperature; both favor increased convection and precipitation. Regression coefficients on precipitation increase from wet to dry conditions. This is consistent with increased uptake of soil water when monthly precipitation is low, until drought conditions are reached, and also consistent with gravity satellite observations. Regression analysis shows monthly opaque cloud cover is tightly correlated to three climate variables that are routinely observed: diurnal temperature range, mean temperature, and mean relative humidity. The set of correlation coefficients, derived from cloud and climate observations, could be used to evaluate the representation of the land-cloud-atmosphere system in both forecast and climate models. [ABSTRACT FROM AUTHOR]

In this paper, an experimental soil moisture drought monitoring and seasonal forecasting framework based on the Variable Infiltration Capacity model (VIC) over southwestern China (SW) is presented. Satellite precipitation data are used to force VIC for a near-real-time estimate of land surface hydrologic conditions. Initialized with satellite-aided monitoring (MONIT), the climate model (CFSv2)-based forecast (MONlT+CFSv2) and ensemble streamflow prediction (ESP)-based forecast (MONIT+ESP) are both performed. One dry season drought and one wet season drought are employed to test the ability of this framework in terms of real-time tracking and predicting the evolution of soil moisture (SM) drought, respectively. The results show that the skillful CFSv2 climate forecasts (CFs) are only found at the first month. The satellite-aided monitoring is able to provide a reasonable estimate of forecast initial conditions (ICs) in real-time mode. In the presented cases, MONIT+CFSv2 forecast exhibits comparable performance against the observation-based estimates for the first month, whereas the predictive skill largely drops beyond 1 month. Compared to MONIT+ESP, MONIT+CFSv2 ensembles give more skillful SM drought forecast during the dry season, as indicated by a smaller ensemble range, while the added value of MONIT+CFSv2 is marginal during the wet season. A quantitative attribution analysis of SM forecast uncertainty demonstrates that SM forecast skill is mostly controlled by ICs at the first month and that uncertainties in CFs have the largest contribution to SM forecast errors at longer lead limes. This study highlights a value of this framework in generating near-real-lime ICs and providing a reliable SM drought prediction with 1 month ahead, which may greatly benefit drought diagnosis, assessment, and early warning. [ABSTRACT FROM AUTHOR]

This paper examines the performance of satellite sounder atmospheric vertical moisture profiles under tropospheric conditions encompassing moisture contrasts driven by convection and advection transport mechanisms, specifically Atlantic Ocean Saharan air layers (SALs), tropical Hadley cells, and Pacific Ocean atmospheric rivers (ARs). Operational satellite sounder moisture profile retrievals from the Suomi National Polar-Orbiting Partnership (SNPP) NOAA Unique Combined Atmospheric Processing System (NUCAPS) are empirically assessed using collocated dedicated radiosonde observations (raobs) obtained from ocean-based intensive field campaigns. The raobs from these campaigns provide uniquely independent correlative truth data not assimilated into numerical weather prediction (NWP) models for satellite sounder validation over oceans. Although ocean cases are often considered "easy" by the satellite remote sensing community, these hydrometeorological phenomena present challenges to passive sounders, including vertical gradient discontinuities (e.g., strong inversions), as well as persistent uniform clouds, aerosols, and precipitation. It is found that the operational satellite sounder 100-layer moisture profile NUCAPS product performs close to global uncertainty requirements in the SAL/Hadley cell environment, with biases relative to raob within 10% up to 350 hPa. In the more difficult AR environment, bias relative to raob is found to be within 20% up to 400 hPa. In both environments, the sounder moisture retrievals are comparable to NWP model outputs, and cross-sectional analyses show the capability of the satellite sounder for detecting and resolving these tropospheric moisture features, thereby demonstrating a near-real-time forecast utility over these otherwise raob-sparse regions. [ABSTRACT FROM AUTHOR]

Extended precipitation forecasts, with leads of weeks to seasons, are valuable for planning water use and are produced by the U.S. National Weather Service. Forecast skill tends to be low and any skill improvement could be valuable. Here, methods are discussed for improving statistical precipitation forecasting over the contiguous United States. Monthly precipitation is forecast using predictors from the previous month. Testing shows that improvements are obtained from both improved statistical methods and from the use of satellite-based ocean-area precipitation predictors. The statistical superensemble method gives higher skill compared to traditional statistical forecasting. Ensemble statistical forecasting combines individual forecasts. The proposed superensemble is a weighted mean of many forecasts or of forecasts from different prediction systems and uses the forecast reliability estimate to define weights. The method is tested with different predictors to show its skill and how skill can be improved using additional predictors. Cross validation is used to evaluate the skill. Although predictions are strongly influenced by ENSO, in the superensemble other regions contribute more to the forecast skill. The superensemble optimally combines forecasts based on different predictor regions and predictor types. The contribution from multiple predictor regions improves skill and reduces the ENSO spring barrier. Adding satellite-based ocean-area precipitation predictors noticeably increases forecast skill. The resulting skill is comparable to that from dynamic-model forecasts, but the regions with best forecast skill may be different. This paper shows that the statistical superensemble forecasts may be complementary to dynamic forecasts and that combining them may further increase forecast skill. [ABSTRACT FROM AUTHOR]

This paper investigates the potential of reanalyses as proxies of observed surface precipitation and temperature to force hydrological models. Three global atmospheric reanalyses (ERA-Interim, CFSR, and MERRA), one regional reanalysis (NARR), and one global meteorological forcing dataset obtained by bias-correcting ERA-Interim [Water and Global Change (WATCH) Forcing Data ERA-Interim (WFDEI)] were compared to one gridded observation database over the contiguous United States. Results showed that all temperature datasets were similar to the gridded observation over most of the United States. On the other hand, precipitation from all three global reanalyses was biased, especially in summer and winter in the southeastern United States. The regional reanalysis precipitation was closer to observations since it indirectly assimilates surface precipitation. The WFDEI dataset was generally less biased than the reanalysis datasets. All datasets were then used to force a global conceptual hydrological model on 370 watersheds of the Model Parameter Estimation Experiment (MOPEX) database. River flows were computed for each watershed, and results showed that the flows simulated using NARR and gridded observations forcings were very similar to the observed flows. The simulated flows forced by the global reanalysis datasets were also similar to the observations, except in the humid continental and subtropical climatic regions, where precipitation seasonality biases degraded river flow simulations. The WFDEI dataset led to better river flows than reanalysis in the humid continental and subtropical climatic regions but was no better than reanalysis-and sometimes worse-in other climatic zones. Overall, the results indicate that global reanalyses have good potential to be used as proxies to observations to force hydrological models, especially in regions with few weather stations. [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]

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]