Spatial Variability Of Precipitation Research Articles

The System for Integrating Multiplatform Data to Build the Atmospheric Column (SIMBA) Precipitation Observation Fusion Framework

AbstractResearchers now have the benefit of an unprecedented suite of space- and ground-based sensors that provide multidimensional and multiparameter precipitation information. Motivated by NASA’s Global Precipitation Measurement (GPM) mission and ground validation objectives, the System for Integrating Multiplatform Data to Build the Atmospheric Column (SIMBA) has been developed as a unique multisensor precipitation data fusion tool to unify field observations recorded in a variety of formats and coordinate systems into a common reference frame. Through platform-specific modules, SIMBA processes data from native coordinates and resolutions only to the extent required to set them into a user-defined three-dimensional grid. At present, the system supports several ground-based scanning research radars, NWS NEXRAD radars, profiling Micro Rain Radars (MRRs), multiple disdrometers and rain gauges, soundings, the GPM Microwave Imager and Dual-Frequency Precipitation Radar on board the Core Observatory satellite, and Multi-Radar Multi-Sensor system quantitative precipitation estimates. SIMBA generates a new atmospheric column data product that contains a concomitant set of all available data from the supported platforms within the user-specified grid defining the column area in the versatile netCDF format. Key parameters for each data source are preserved as attributes. SIMBA provides a streamlined framework for initial research tasks, facilitating more efficient precipitation science. We demonstrate the utility of SIMBA for investigations, such as assessing spatial precipitation variability at subpixel scales and appraising satellite sensor algorithm representation of vertical precipitation structure for GPM Core Observatory overpass cases collected in the NASA Wallops Precipitation Science Research Facility and the GPM Olympic Mountain Experiment (OLYMPEX) ground validation field campaign in Washington State.

High resolution climate precipitation analysis for north-central Italy, 1961–2015

Observational daily precipitation data from a group of 1762 stations over north-central Italy and adjacent areas are used to produce a high resolution daily gridded precipitation analysis covering the period from 1961 to 2015. Input data are checked for quality, time consistency, synchronicity and statistical homogeneity and the final result has been used to describe the spatial and temporal variability of precipitation over the area. Data are interpolated using a modified Shepard scheme and the interpolation errors are compatible with those presented in Isotta et al. (Int J Climatol 34(5):1657–1675, 2014). The analysis is compared with other similar products available over the area considered, and differences and similarities are described, taking into account the impacts of different spatial resolution and time coverage. The data set is used to describe local climate with respect to precipitation, including mean values and seasonality, by using a group of climate annual and seasonal indices: cumulated precipitation, maximum number of consecutive dry days, frequency of wet days, mean precipitation intensity and 50th and 90th percentile of daily precipitation over a season. The linear trends over the full period of these indices are described and compared. It is shown that although the time series of area average total annual precipitation over north-central Italy does not show significant linear trends, these are present locally. In particular, significant negative trends of annual total precipitation are found in central Italy and in the inner part of northern plains, while significant positive linear trends are present in several areas over the Alps and over the Liguria coast. The seasons most affected by changes in precipitation are summer and autumn, which, in most areas, are the driest and wettest seasons. In summer, significant positive trends in total precipitation have been found in areas close to the northern national borders, while significant negative trends are located elsewhere. The number of wet days is significantly decreasing over most of the domain, but the 90th percentile of precipitation is significantly increasing over most of the Alpine area and northern Po Valley. Over the southern part of the Po Valley and central Italy summer precipitation is significantly becoming less frequent and, generally, less intense. In autumn, total precipitation is characterised by significant positive trends over large areas in Northern Italy and by significant negative trends in inner areas of the Central Apennines. The trend patterns present great similarities with those of the 90th percentile of daily precipitation for the same season. The maximum length of dry spell is significantly decreasing in autumn over most areas, including central Italy, while the number of wet days presents negative but mostly non significant trends over the whole domain.

Diagnosis of deformation-derived ascending areas in a rainband

This paper demonstrates that, for a moist baroclinic frontal system, the large-value deformation belt in the low-level atmosphere overlaps with precipitation. To precisely describe the relationship between deformation and heavy precipitation, deformation is introduced into the non-geostrophic Q#-vector. Q# is then decomposed into three parts: the divergence-related term, the vorticity-related term, and the deformation-related term. By calculating the divergence of Q# and its components, it is found that in strong ascending areas within precipitation regions the non-geostrophic Q#-vector divergence shows strong negative values. Its deformational component can contribute about 68% to these negative values. This verifies that strong deformation in a precipitating atmosphere is favorable for the development of convection and precipitation. In addition, by calculating the correlation coefficients between the Q#-vector (including its components) divergence and vertical motions, it is also found that the Q#-vector divergence shows higher correlation with vertical motion within the precipitation belt and lower correlation in the non-precipitation areas, which indicates a larger contribution of Q# to vertical motion when precipitation occurs and implies an effect of Q# to the precipitation distribution or spatial variability. Among the three components of the Q#-vector, the correlation coefficients between the deformational component and vertical motion are the most similar in pattern to that of the correlation coefficients between the Q#-vector and vertical motion, which further reflects the important contribution of deformation to the large spatial variability of precipitation.

Relationships between variability in precipitation, river levels, and beef cattle production in the Brazilian Pantanal

The hydrology of the Brazilian Pantanal, part of the largest humid tropical area on the planet, follows the rhythm of seasonal floods. The traditional movement of cattle herds in the southern Pantanal is defined by these seasonal flooding patterns, which determine the availability and quality of native-grass pastures throughout the year. Extreme hydrological events, such as prolonged droughts and intense floods, can impact the management of cattle in this region, preventing access to, circulation within, and occupation of some grazing areas. This study aims to analyze the spatial and temporal variability of precipitation, the river levels in this region from 1974 to 2012, and assessed the effects on the evolution of local beef cattle stocks. We integrated the spatiotemporal variability of precipitation and river levels for six gaging stations and standardized these variables using standard deviation and linear trends at annual and seasonal scales. Additionally, we integrated the results with an analysis of the livestock production within the region’s municipalities. Regional precipitation was highly variable, including an almost decadal oscillation, with positive trends in parts of the 1970s and 1980s and a negative trend since the 1990s. River levels in the northern portion of the basin and in the Paraguay River corresponded to those dynamics while presenting marked anomalies. Simultaneously, river levels in the eastern Pantanal changed from negative to positive anomalies, which were more intense in wet months. Both the eastern and western patterns reflected the occurrence of extreme El Nino–Southern Oscillation events and other large-scale climatic patterns. In intra-annual terms, the variability of the river levels during the flooded months at four of the six gaging stations studied suggests an early onset or delay of the flood season, as well as positive anomalies in the ebbing months and in the southeast. Given the lags in flow between different rivers in the region, this behavior draws attention to dam construction and intensification of localized flooding in the southern areas, which may impact everyday life and cattle management. For example, from 2009 to 2012, two severe droughts and an extreme flood significantly impacted livestock numbers in the region. Overall, herd numbers decreased (increased) in humid (dry) periods, including a reduction of over 1 million head during the flood of 1973–1974. The differential level patterns at annual, wet season, and dry season between the eastern and western sides of the Pantanal modulate the livestock activities, where the strong negative anomalies of the levels along the eastern side seem to force the diminution of beef cattle stock at local regions in different intensities in annual scale. This reinforces the effects of climate variability and extreme hydrological events on the management and dynamics of the beef cattle industry and market in Brazil.

Spatial Downscaling of Satellite-Based Precipitation and Its Impact on Discharge Simulations in the Magdalena River Basin in Colombia

Precipitation is one of the most important components of the water cycle and its accurate spatial and temporal representation is fundamental for hydrological modeling. In the present study, we investigated the impact of spatial resolution of various precipitation datasets on discharge estimates. First, a new precipitation spatial downscaling procedure was developed and applied to four gridded global precipitation datasets based on (i) solely satellite observations: CMORPH and PERSIANN, (ii) satellite and in situ observations: TRMM and (iii) satellite and in situ observations and reanalysis data: MSWEP. The here presented downscaling methodology blended global precipitation datasets with data on vegetation and topography to improve the representation of precipitation spatial variability. Second, interpolated in situ, non-downscaled (25 km) and downscaled (1 km) precipitation data were used to force a grid-distributed version of the HBV-96 rainfall-runoff model for the Magdalena River basin in Colombia. Results showed that MSWEP and TRMM outperformed CMORPH and PERSIANN precipitation datasets. The downscaling procedure resulted in considerable improvements in coefficient of determination, root mean square error and bias in comparison with in situ precipitation observations. Discharge model estimates were also in better agreement with the observations when the model was forced with the downscaled precipitation. Model performance was improved with Kling Gupta efficiency increases in the order of 0.1 to 0.5. Moreover, better discharge simulations were obtained using downscaled precipitation compared to using only in situ precipitation data when using less than 100 stations.

Connections between meteorological and hydrological droughts in a semi-arid basin of the middle Yellow River

Abstract. Differences between meteorological and hydrological droughts could reflect the regional water consumption by both natural elements and human water-use. The connections between these two drought types were analyzed using the Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Streamflow Index (SSI), respectively. In a typical semi-arid basin of the middle Yellow River (Qingjianhe River basin), annual precipitation and air temperature showed significantly downward and upward trends, respectively, with the rates of −2.37 mm yr−1 and 0.03 ∘C yr−1 (1961–2007). Under their synthetic effects, water balance variable (represented by SPEI) showed obviously downward (drying) trend at both upstream and whole basin areas. For the spatial variability of precipitation, air temperature and the calculated SPEI, both upstream and downstream areas experienced very similar change characteristics. Results also suggested that the Qingjianhe River basin experienced near normal condition during the study period. As a whole, this semi-arid basin mainly had the meteorological drought episodes in the mid-1960s, late-1990s and the 2000s depicted by 12-month SPEI. The drying trend could also be depicted by the hydrological drought index (12-month SSI) at both upstream and downstream stations (Zichang and Yanchuan), but the decreasing trends were not significant. A correlation analysis showed that hydrological system responds rapidly to the change of meteorological conditions in this semi-arid region. This finding could be an useful implication to drought research for those semi-arid basins with intensive human activities.

Temporal and spatial variation of precipitation in the Hengduan Mountains region in China and its relationship with elevation and latitude

The temporal and spatial variations in precipitation, and the relationships between precipitation and elevation/latitude are analyzed. The following results are derived from the analysis: (1) Unimodal and bimodal precipitation regimes are observed for 19 and 11 stations, respectively, of which the Type 1 and Type 2 bimodal precipitation regime is observed for three and eight stations, respectively. The primary flood season of the Type 1 bimodal precipitation regime is around July, except for Gongshan station that is relatively long flood season. The primary flood season of the Type 2 bimodal precipitation regime is around June. The precipitation peaks are negatively correlated with latitude/elevation for each bimodal precipitation regime. (2) Basically similar spatial distributions are observed for the annual and seasonal average precipitation — the southern part of the Hengduan Mountains region experiences relatively high precipitation, whereas the central and northeastern parts experience relatively low precipitation; in addition, the precipitation trends are nonsignificant for most of the stations. A decreasing trend is observed for the annual, summer and drought season precipitation for most of the stations located in the area south of 28°N, whereas an increasing trend is observed for most of the stations located in other areas. Most stations experience an increasing in spring precipitation, and in autumn, the opposite is observed. A decreasing trend is observed for winter precipitation for most of the stations except for the northeastern part of the study area. An increasing trend is observed for precipitation in the rain season for most of the stations except for the northeastern and southern parts of the study area. Significantly negative correlations are observed between the annual and seasonal with relatively high precipitation (summer, autumn and the rain season) average precipitation and elevation/latitude (3) Negative correlations, mostly significant, are evident between the standard deviations of the annual, seasonal and monthly precipitation and elevation/latitude.

A 1286‐year hydro‐climate reconstruction for the Balkan Peninsula

We present a June–July drought reconstruction based on the standardized precipitation index (SPI) for the Balkan Peninsula over the period 730–2015 CE. The reconstruction is developed using a composite Pinus heldreichii tree‐ring width chronology, from a high‐elevation network of eight sites in the Pindus Mountains in northwest Greece, composed of living trees and relict wood. The dataset includes the ring width series of Europe's currently oldest known living tree, dendrochronologically dated to be more than 1075 years old. The spatial coverage of the reconstruction is improved by using an averaged gridded SPI data target derived from a response field that is located north of the study region. Justification for this approach includes the remoteness of instrumental data, the spatial variability of precipitation and synoptic scale circulation patterns. Over the past 1286 years, there have been 51 dry and 43 pluvial events. The driest year during the 1286‐year‐long period was 1660 and the wettest year was 1482. Comparison with shorter reconstructions and documentary evidence validates the new reconstruction, and provides additional insight into socioeconomic impacts and spatial patterns of extreme events. Fifty‐nine of 72 previously undescribed extremes occurred prior to the 17th century. The new reconstruction reveals long‐term changes in the number of extremes, including substantially fewer drought and pluvial events in the 20th century. Additional tests on the long‐term effects of age structure, replication and covariance changes support the heteroscedastic nature of the reconstructed hydro‐climatic extremes.

Arc-Malstrøm: A 1D hydrologic screening method for stormwater assessments based on geometric networks

A method is presented to perform a first screening of high-resolution digital terrain models to detect the extents and capacities of local landscape sinks. When identified their capacities and rain volumes provided from their local catchments during a rainstorm are saved as attributes for the pour points. Next, the downstream paths from the pour points are saved as junctions and edges in a geometric network leading to a final calculation of the accumulated spillover in the topologic data structure determined by a custom trace tool. Although the screening method is based on a representation of the overland surface in a 1D network not involving any hydrodynamic components, it is well suited to provide a quick first overview of a landscape's overall drainage basins, the location of sinks, their contributing watersheds and the accumulated downstream flow when the sinks spill over. The exemplified study assumes Hortonian flow and a uniform rain event, but if spatial variations in precipitation or infiltration capacities are available the local sinks' catchment level, they may easily be added to the workflow to produce first risk map approximations for residential areas threatened by future stormwater incidents.

Responses of runoff to historical and future climate variability over China

Abstract. China has suffered some of the effects of global warming, and one of the potential implications of climate warming is the alteration of the temporal–spatial patterns of water resources. Based on the long-term (1960–2008) water budget data and climate projections from 28 global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5), this study investigated the responses of runoff (R) to historical and future climate variability in China at both grid and catchment scales using the Budyko-based elasticity method. Results show that there is a large spatial variation in precipitation (P) elasticity (from 1.1 to 3.2) and potential evaporation (PET) elasticity (from −2.2 to −0.1) across China. The P elasticity is larger in north-eastern and western China than in southern China, while the opposite occurs for PET elasticity. The catchment properties' elasticity of R appears to have a strong non-linear relationship with the mean annual aridity index and tends to be more significant in more arid regions. For the period 1960–2008, the climate contribution to R ranges from −2.4 to 3.6 % yr−1 across China, with the negative contribution in north-eastern China and the positive contribution in western China and some parts of the south-west. The results of climate projections indicate that although there is large uncertainty involved in the 28 GCMs, most project a consistent change in P (or PET) in China at the annual scale. For the period 2071–2100, the mean annual P is projected to increase in most parts of China, especially the western regions, while the mean annual PET is projected to increase in all of China, particularly the southern regions. Furthermore, greater increases are projected for higher emission scenarios. Overall, due to climate change, the arid regions and humid regions of China are projected to become wetter and drier in the period 2071–2100, respectively (relative to the baseline 1971–2000).

Variability of thermal and precipitation conditions in the growing season in Poland in the years 1966\u20132015

The aim of the study was to identify the thermal and precipitation conditions and their changes in the growing season in Poland in the years 1966–2015. Data on average daily air temperature and daily precipitation totals for 30 stations from the period of 1966–2015 were used. The data were obtained from the collections of the Institute of Meteorology and Water Management—National Research Institute. The growing season was defined as the period of average daily air temperature ≥ 5 °C. The mathematical formulas proposed by Gumiński (1948) were used to determine its start and end dates. In the growing season in Poland in the years 1966–2015, there were more significant changes in the thermal conditions than there were in the precipitation conditions. In terms of long-term trends over the study period, thermal conditions during the growing season are characterised by an increase in mean air temperature, an increase in the sum of air temperatures and an increasing occurrence of seasons classified as above-normal seasons. Precipitation conditions of the growing season show large temporal and spatial variations in precipitation and a predominance of normal conditions. The changes in precipitation were not statistically significant, except for Świnoujście.

Spatial interpolation of climate variables in Northern Germany—Influence of temporal resolution and network density

Study regionRegion in Lower Saxony (North Germany) covered by the measuring range of the weather radar device located near Hanover (approx. 50.000 m2). Study focusThis study investigates the performance of various spatial interpolation techniques for climate variables. Meteorological observations are usually recorded as site-specific point information by weather stations and estimation accuracy for unobserved locations depends generally on station density, temporal resolution, spatial variation of the variable and choice of interpolation method. This work aims to evaluate the influence of these factors on interpolation performance of different climate variables. A cross validation analysis was performed for precipitation, temperature, humidity, cloud coverage, sunshine duration, and wind speed observations. Hourly to yearly temporal resolutions and different additional information were considered. New hydrological insightsGeostatistical techniques provide a better performance for all climate variables compared to simple methods Radar data improves the estimation of rainfall with hourly temporal resolution, while topography is useful for weekly to yearly values and temperature in general. No helpful information was found for cloudiness, sunshine duration, and wind speed, while interpolation of humidity benefitted from additional temperature data. The influences of temporal resolution, spatial variability, and additional information appear to be stronger than station density effects. High spatial variability of hourly precipitation causes the highest error, followed by wind speed, cloud coverage and sunshine duration. Lowest errors occur for temperature and humidity.

Long-term variability of air temperature and precipitation conditions in the Polish Carpathians

Mountain regions are sensitive to climate changes, which make them good indicators of climate change. The aim of this study is to investigate the spatial and temporal variability of air temperature and precipitation in the Polish Carpathians. This study consists of climatological analyses for the historical period 1851-2010 and future projections for 2021-2100. The results confirm that there has been significant warming of the area and that this warming has been particularly pronounced over the last few decades and will continue in the oncoming years. Climate change is most evident in the foothills; however, these are the highest summits which have experienced the most intensive increases in temperature during the recent period. Precipitation does not demonstrate any substantial trend and has high year-to-year variability. The distribution of the annual temperature contour lines modelled for selected periods provides evidence of the upward shift of vertical climate zones in the Polish Carpathians, which reach approximately 350 meters, on average, what indicates further ecological consequences as ecosystems expand or become extinct and when there are changes in the hydrological cycle.

Rainfall zoning of Bahia State, Brazil: an update proposal

The state of Bahia’s main climatic characteristic is the high spatial and chronological variability of precipitation. This heterogeneity may be used to determine of pluviometrically homogeneous areas that can define mesoregions in the state, since they allow better management of water resources and help in the elaboration of agricultural studies. The mesoregions already proposed by the scientific community for the state were based only on the annual precipitation in the proximity of the pluviometric stations. In this paper, besides these parameters, spatial and chronological rainfall distribution was considered, i.e., the Precipitation Concentration Degree (PCD) and Precipitation Concentration Period (PCP). The new zoning is based on an update of a study defined in 2000 that divided Bahia into eight mesoregions. Thus, 180 pluviometric stations were distributed throughout the state and grouped conforming to the division previously described. It was concluded that some stations of the same mesoregion had presented conflicting values for the analyzed parameters and, therefore, should not belong to the same area. Starting from an arrangement of the collection stations, considering their proximity, annual precipitation and statistical parameters, a new zoning for Bahia with 10 clusters was defined and validated through the statistical treatment of data.

Spatial and Temporal Variability in the Precipitation Concentration in the Upper Reaches of the Hongshui River Basin, Southwestern China

The statistical characteristics of precipitation play important roles not only in flood and drought risk assessments but also in water resource management. This paper implements a statistical analysis to study the spatial and temporal variability in precipitation in the upper reaches of the Hongshui River basin (UHRB), southwestern China, by analysing time series of daily precipitation from 18 weather stations during the period of 1959 to 2015. To detect precipitation concentrations and the associated patterns, three indices, the precipitation concentration index (PCI), precipitation concentration degree (PCD), and precipitation concentration period (PCP), were used. The relationships between the precipitation concentration indices (PCI, PCD, and PCP) and geographic variables (latitude, longitude, and elevation), large-scale atmospheric circulation indices, and summer monsoon indices were investigated to identify specific dependencies and spatial patterns in the precipitation distribution and concentration. The results show that high PCI values were mainly observed in the northeastern portion of the basin, whereas low PCI values were mainly detected in the southwest. The Mann-Kendall test results demonstrate that the majority of the UHRB is characterized by nonsignificant trends in the PCI, PCD, and PCP from 1959 to 2015. The PCP results reveal that rainfall in the UHRB mainly occurs in summer months, and the rainy season arrives earlier in the eastern UHRB than in the western UHRB. Additionally, the PCD results indicate that the rainfall in the western UHRB is more dispersed throughout the year than that in the eastern UHRB. Compared with other geographical factors, longitude is the most important variable that governs the spatial distribution and variations in annual precipitation and the precipitation concentration indices. Due to a combination of topography, the Indian subtropical high, and monsoon weakening, precipitation may be more concentrated in one period, especially in the eastern part of the basin, which increases the risk of drought.

Identification of trend in long term precipitation and reference evapotranspiration over Narmada river basin (India)

Abstract Precipitation and reference evapotranspiration are key parameters in hydro-meteorological studies and used for agricultural planning, irrigation system design and management. Precipitation and evaporative demand are expected to be alter under climate change and affect the sustainable development. In this article, spatial variability and temporal trend of precipitation and reference evapotranspiration (ETo) were investigated over Narmada river basin (India), a humid tropical climatic region. In the present study, 12 and 28 observatory stations were selected for precipitation and ETo, respectively of 102-years period (1901–2002). A rigorous analysis for trend detection was carried out using non parametric tests such as Mann-Kendall (MK) and Spearman Rho (SR). Sen's slope estimator was used to analyze the rate of change in long term series. Moreover, all the stations of basin exhibit positive trend for annual ETo, while 8% stations indicate significant negative trend for mean annual precipitation, respectively. Change points of annual precipitation were identified around the year 1962 applying Buishand's and Pettit's test. Annual mean precipitation reduced by 9% in upper part while increased maximum by 5% in lower part of the basin due temporal changes. Although annual mean ETo increase by 4–12% in most of the region. Moreover, results of the study are very helpful in planning and development of agricultural water resources.

Modeling the Spatial and Temporal Variability of Precipitation in Northwest Iran

Spatial and temporal variability analysis of precipitation is an important task in water resources planning and management. This study aims to analyze the spatial and temporal variability of precipitation in the northeastern corner of Iran using data from 24 well-distributed weather stations between 1991 and 2015. The mean annual rainfall, precipitation concentration index (PCI), and their coefficients of variation were mapped to examine the spatial variability of rainfall. An artificial neural network (ANN) in association with the inverse distance weighted (IDW) method was proposed as a hybrid interpolation method to map the spatial distribution of the detected trends of mean annual rainfall and PCI over the study region. In addition, principal component analysis (PCA) was applied to annual precipitation time series in order to verify the results of the analysis using the mean annual rainfall and PCI data sets. Results show high variation in inter-annual precipitation in the west, and a moderate to high intra-annual variability over the whole region. Irregular year-to-year precipitation concentration is also observed in the northeastern and northwestern parts. All in all, the highest variations in inter-annual and intra-annual precipitation occurred over the western and northern parts, while the lowest variability was observed in the eastern part (i.e., the coastal region).

Scale dependency of regional climate modeling of current and future climate extremes in Germany

A warmer climate is projected for mid-Europe, with less precipitation in summer, but with intensified extremes of precipitation and near-surface temperature. However, the extent and magnitude of such changes are associated with creditable uncertainty because of the limitations of model resolution and parameterizations. Here, we present the results of convection-permitting regional climate model simulations for Germany integrated with the COSMO-CLM using a horizontal grid spacing of 1.3 km, and additional 4.5- and 7-km simulations with convection parameterized. Of particular interest is how the temperature and precipitation fields and their extremes depend on the horizontal resolution for current and future climate conditions. The spatial variability of precipitation increases with resolution because of more realistic orography and physical parameterizations, but values are overestimated in summer and over mountain ridges in all simulations compared to observations. The spatial variability of temperature is improved at a resolution of 1.3 km, but the results are cold-biased, especially in summer. The increase in resolution from 7/4.5 km to 1.3 km is accompanied by less future warming in summer by 1 ∘C. Modeled future precipitation extremes will be more severe, and temperature extremes will not exclusively increase with higher resolution. Although the differences between the resolutions considered (7/4.5 km and 1.3 km) are small, we find that the differences in the changes in extremes are large. High-resolution simulations require further studies, with effective parameterizations and tunings for different topographic regions. Impact models and assessment studies may benefit from such high-resolution model results, but should account for the impact of model resolution on model processes and climate change.

Global Multiscale Evaluation of Satellite Passive Microwave Retrieval of Precipitation during the TRMM and GPM Eras: Effective Resolution and Regional Diagnostics for Future Algorithm Development

Abstract 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.

Providing a non-deterministic representation of spatial variability of precipitation in the Everest region

Abstract. This paper provides a new representation of the effect of altitude on precipitation that represents spatial and temporal variability in precipitation in the Everest region. Exclusive observation data are used to infer a piecewise linear function for the relation between altitude and precipitation and significant seasonal variations are highlighted. An original ensemble approach is applied to provide non-deterministic water budgets for middle and high-mountain catchments. Physical processes at the soil–atmosphere interface are represented through the Interactions Soil–Biosphere–Atmosphere (ISBA) surface scheme. Uncertainties associated with the model parametrization are limited by the integration of in situ measurements of soils and vegetation properties. Uncertainties associated with the representation of the orographic effect are shown to account for up to 16 % of annual total precipitation. Annual evapotranspiration is shown to represent 26 % ± 1 % of annual total precipitation for the mid-altitude catchment and 34% ± 3 % for the high-altitude catchment. Snowfall contribution is shown to be neglectable for the mid-altitude catchment, and it represents up to 44 % ± 8 % of total precipitation for the high-altitude catchment. These simulations on the local scale enhance current knowledge of the spatial variability in hydroclimatic processes in high- and mid-altitude mountain environments.