Precipitation In Mountainous Regions Research Articles

Probabilistic Forecasts of Atmospheric River events using the HRRR Ensemble

The nine-member High-Resolution Rapid Refresh Ensemble (HRRRE) is evaluated for its ability to forecast five Atmospheric River (AR) events that impacted California in February–March 2019. Two sets of retrospective HRRRE simulations are conducted, a control with the standard set of perturbations (initial and boundary conditions, stochastic parameters, and physics tendency), and an experiment with initial and boundary perturbations only. Reliability plots suggest the HRRRE control represents the observed Stage IV precipitation frequency well at 6-h to 24-h lead times, and rank histograms suggest the ensemble is slightly underdispersive. The HRRRE overpredicts precipitation frequency at the higher (25 mm) threshold. These results suggest the HRRRE is a useful tool to quantify probabilistic forecasts of AR events in this region. Removing stochastic physics perturbations did not substantially impact probabilistic forecasts, suggesting most of the ensemble spread is from initial and boundary condition perturbations. Spatially, ensemble precipitation coefficient of variance is lower (less forecast uncertainty) over the Sierra Nevada range than other regions, suggesting that these ensemble perturbations have a smaller impact on precipitation processes occurring over the Sierra Nevada range. More work should be conducted to understand the impacts of other model perturbations, such as microphysics, on ensemble performance, and to improve Stage IV accuracy with frozen precipitation in mountainous regions.

A variance-upscaling quantile mapping method for gridded precipitation bias correction

Accurate estimation of precipitation in mountainous regions remains a challenge. Quantile Mapping (QM) is a method used extensively to correct gridded precipitation from numerical models or satellite remote sensing. The method can match the intensity histogram of a given gridded precipitation to that of the observation. However, the widely existing oversmoothed effect, a precipitation time series variability representativeness error, in observational gridded datasets may be inherited in the corrected results, impacting the assessment of extreme precipitation events. To address this issue, this study introduces a variance-upscaling QM method (VUQM). Unlike previous methods that obtain frequency parameters by interpolating rain gauge data, the new method creates a model to transform the precipitation time series variance at the rain gauge point level to the model's grid level. When compared to correct ERA5-Land reanalysis precipitation, the results show that the variance using the VUQM is 1.6 times larger than that obtained using QM alone, and it is 2 times larger in the 95th percentile. This indicates that the new method significantly reduces the oversmoothed effect and preserves precipitation variation. Random simulations show that low station density only introduces uncertainty, not theoretical bias in VUQM. In contrast, the variance biases in gridded datasets constructed from areal average or interpolation cannot be avoided, particularly in regions with significant spatial heterogeneity. This suggests that VUQM is well-suited for use in the Qinghai-Tibetan Plateau (QTP) compared with the old methods. Overall, this method sheds some light on mountainous precipitation bias correction.

Investigating the Performance of CMIP6 Seasonal Precipitation Predictions and a Grid Based Model Heterogeneity Oriented Deep Learning Bias Correction Framework

AbstractClimate change is expected to alter the magnitude and spatiotemporal patterns of hydro‐climate variables such as precipitation, which has significant impacts on the ecosystem, human societies and water security. Global Climate Models are the major tools to simulate historical as well as future precipitation. However, due to imperfect model structures, parameters and boundary conditions, direct model outputs are subject to large uncertainty, which needs serious evaluation and bias correction before usage. In this study, seasonal precipitation predictions from 30 Coupled Model Inter‐comparison Project Phase 6 (CMIP6) models and Climate Research Unit observations are used to evaluate historical precipitation climatology in global continents during 1901–2014. A grid based model heterogeneity oriented Convolutional Neural Network (CNN) is proposed to correct the ensemble mean precipitation bias ratio. Besides, regression based Linear Scaling (LS), distribution based Quantile Mapping (QM) and spatial correlation CNN bias correction approaches are employed for comparison. Results of model performance evaluation indicate that generally precipitation prediction is more reliable in JJA than DJF on the global scale. Most models tend to have larger bias ratio for extreme precipitation. In addition, current CMIP6 models still have certain issues in accurate simulation of precipitation in mountainous regions and the regions affected by complex climate systems. Moreover, the proposed grid based model heterogeneity oriented CNN has better performance in ensemble mean bias correction than LS, QM, and spatial correlation CNN, which could consider the relative model performance and capture the features similar to actual climate dynamics.

Identifying snowfall elevation patterns by assimilating satellite-based snow depth retrievals

Precipitation in mountain regions is highly variable and poorly measured, posing important challenges to water resource management. Traditional methods to estimate precipitation include in-situ gauges, Doppler weather radars, satellite radars and radiometers, numerical modeling and reanalysis products. Each of these methods is unable to adequately capture complex orographic precipitation. Here, we propose a novel approach to characterize orographic snowfall over mountain regions. We use a particle batch smoother to leverage satellite information from Sentinel-1 derived snow depth retrievals and to correct various gridded precipitation products. This novel approach is tested using a simple snow model for an alpine basin located in Trentino Alto Adige, Italy. We quantify the precipitation biases across the basin and found that the assimilation method (i) corrects for snowfall biases and uncertainties, (ii) leads to cumulative snowfall elevation patterns that are consistent across precipitation products, and (iii) results in overall improved basin-wide snow variables (snow depth and snow cover area) and basin streamflow estimates.

Performance Evaluation of TerraClimate Monthly Rainfall Data after Bias Correction in the Fes-Meknes Region (Morocco)

Morocco’s meteorological observation network is quite old, but the spatial coverage is insufficient to conduct studies over large areas, especially in mountainous regions, such as the Fez-Meknes region, where spatio-temporal variability in precipitation depends on altitude and exposure. The lack of station data is the main reason that led us to look for alternative solutions. TerraClimate (TC) reanalysis data were used to remedy this situation. However, reanalysis data are usually affected by a bias in the raw values. Bias correction methods generally involve a procedure in which a “transfer function” between the simulated and corrected variable is derived from the cumulative distribution functions (CDFs) of these variables. We explore the possibilities of using TC precipitation data for the Fez-Meknes administrative region (Morocco). This examination is of great interest for the region whose mountain peaks constitute the most important reservoir of water in the country, where TC data can overcome the difficulty of estimating precipitation in mountainous regions where the spatio-temporal variability is very high. Thus, we carried out the validation of TC data on stations belonging to plain and mountain topographic units and having different bioclimatic and topographic characteristics. Overall, the results demonstrate that the TC data capture the altitudinal gradient of precipitation and the average rainfall pattern, with a maximum in November and a minimum in July, which is a characteristic of the Mediterranean climate. However, we identified quasi-systematic biases, negative in mountainous regions and positive in lowland stations. In addition, summer precipitation is overestimated in mountain regions. It is considered that this bias comes from the imperfect representation of the physical processes of rainfall formation by the models. To reduce this bias, we applied the quantile mapping (QM) method. After correction using five QM variants, a significant improvement was observed for all stations and most months, except for May. Validation statistics for the five bias correction variants do not indicate the superiority of any particular method in terms of robustness. Indeed, results indicate that most QM methods lead to a significant improvement in TC data after monthly bias corrections.

How well does a convection-permitting regional climate model represent the reverse orographic effect of extreme hourly precipitation?

Abstract. Estimating future short-duration extreme precipitation in mountainous regions is fundamental for risk management. High-resolution convection-permitting models (CPMs) represent the state of the art for these projections, as they resolve convective processes that are key to short-duration extremes. Recent observational studies reported a decrease in the intensity of extreme hourly precipitation with elevation. This “reverse orographic effect” could be related to processes which are subgrid even for CPMs. To quantify the reliability of future projections of extreme short-duration precipitation in mountainous regions, it is thus crucial to understand to what extent CPMs can reproduce this effect. Due to the computational demands however, CPM simulations are still too short for analyzing extremes using conventional methods. We use a non-asymptotic statistical approach (Simplified Metastatistical Extreme Value: SMEV) for the analysis of extremes from short time periods, such as the ones of CPM simulations. We analyze an ERA-Interim-driven Consortium for Small-Scale Modeling (COSMO-crCLIM, convection-resolving Climate Modelling) simulation (2000–2009; 2.2 km resolution), and we use hourly precipitation from 174 rain gauges in an orographically complex area in northeastern Italy as a benchmark. We investigate the ability of the model to simulate the orographic effect on short-duration precipitation extremes, as compared to observational data. We focus on extremes as high as the 20-year return levels. While overall good agreement is reported at daily and hourly duration, the CPM tends to increasingly overestimate hourly extremes with increasing elevation, implying that the reverse orographic effect is not fully captured. These findings suggest that CPM bias-correction approaches should account for orography. SMEV's capability of estimating reliable rare extremes from short periods promises further applications on short-time-period CPM projections and model ensembles.

Climate change creates opportunities to expand agriculture in the Hindu Kush Himalaya but will cause considerable ecosystem trade-offs

Theoretically, climate change will create warmer temperatures and greater precipitation in mountainous regions, making agriculture possible in areas that were once unsuitable for cropping. But the extent and the nature of these ‘agricultural frontiers’ is as yet unknown. Building upon recent research on Climate Change Driven Agricultural Frontiers [CCDAFs], this paper assesses the potential of agricultural expansion in the Hindukush Himalaya [HKH]. Using FAO crop suitability data, we estimated the extent of CCDAFs under three Representative Concentration Pathways for 13 crops as well as the potential impacts of developing these frontiers on ecosystem services. We show that under climate change projected by the IPSL- CM5A-LR climate model, 34,507 km2 of agricultural frontiers may emerge in the HKH by 2100 under RCP 6.0. Additionally, results suggest that there will be new opportunities for crop diversification as individual crops will gain frontier area. However, developing these CCDAFs will impact supportive and regulating ecosystem services including carbon storage and sequestration, soil quality, biodiversity, and hydrological processes—with implications for regional water security. These impacts must be considered alongside the benefits of additional food production when evaluating the net benefits of developing CCDAFS.

Ocean fronts and eddies force atmospheric rivers and heavy precipitation in western North America

Atmospheric rivers (ARs) are responsible for over 90% of poleward water vapor transport in the mid-latitudes and can produce extreme precipitation when making landfall. However, weather and climate models still have difficulty simulating and predicting landfalling ARs and associated extreme precipitation, highlighting the need to better understand AR dynamics. Here, using high-resolution climate models and observations, we demonstrate that mesoscale sea-surface temperature (SST) anomalies along the Kuroshio Extension can exert a remote influence on landfalling ARs and related heavy precipitation along the west coast of North America. Inclusion of mesoscale SST forcing in the simulations results in approximately a 40% increase in landfalling ARs and up to a 30% increase in heavy precipitation in mountainous regions and this remote impact occurs on two-week time scales. The asymmetrical response of the atmosphere to warm vs. cold mesoscale SSTs over the eddy-rich Kuroshio Extension region is proposed as a forcing mechanism that results in a net increase of moisture flux above the planetary boundary layer, prompting AR genesis via enhancing moisture transport into extratropical cyclones in the presence of mesoscale SST forcing.

Accounting for elevation and distance to the nearest coastline in geostatistical mapping of average annual precipitation

Spatial distribution of precipitation in mountainous areas suggests strong control of surface orography on precipitation processes. Generally, quantifying orographic control of precipitation and identifying homogeneous areas are difficult because of the complex combination of factors, which could influence the precipitation process. The objective of this study was to account for morphometric attributes (elevations and distances to the nearest coastline) in geostatistical mapping of average annual precipitation in southern Italy. The study area was the Calabria Region, which has a spatially variable Mediterranean climate because of its high orographic variability. In this study, annual precipitation data collected by the former Italian Hydrographic Service for the 1916–2006 period were used. Elevations and distances to the nearest coastline were derived from a digital elevation model with 250 m × 250 m cell size in a geographic information system environment and used to delineate areas with homogeneous morphological features [landscape units (LU)]. The effectiveness of LU was assessed estimating the expected value of the average annual precipitation with polygon kriging and comparing their differences with the Mann–Whitney–Wilcoxon test. The average annual precipitation map showed that mountains areas receive more precipitation than low elevation areas and, in the Tyrrhenian side, it was also evident the orographic influence of Coastal chain on precipitation with high precipitation values. Results can help in understanding the differences among LU and the influence of surface orography on spatial patterns of annual precipitation in mountainous regions.

Evaluation of the North American Regional Reanalysis (NARR) precipitation fields in a topographically complex domain

ABSTRACTThe North American Regional Reanalysis (NARR) precipitation product was evaluated using station observations and catchment water yield in British Columbia (BC), Canada, at inter-annual, monthly, and daily time scales. A structural break occurred in 2003, associated with exclusion of Canadian precipitation gauge data from NARR’s data assimilation process beginning in that year. The NARR product under-predicted precipitation in mountainous regions, over-predicted in the northern region of BC’s Interior Plateau, and catchment-averaged NARR precipitation was less than observed water yield in coastal BC. The product was unable to reproduce even the seasonal pattern at three stations. This study highlights uncertainties associated with the NARR precipitation product, and presents a cautionary tale that is likely relevant not only to the application of NARR in BC, but may be relevant for other re-analysis products and other regions with complex topography and sparse station networks.

The role of orographic barriers in the origin of extreme rainfalls as exemplified by the front of high Eastern Himalaya and the low northern slope of Carpathians

The paper discusses the role of orographic barriers in generating torrential precipitation in mountainous regions in different climatic zones, the Eastern Himalayas (tropical zone with well-developed monsoon activity) and the northern slope of the Carpathians (temperate zone with transitional climate). Attention has been paid to the different altitudes and courses of the orographic ridges as well as their location relative to the prevailing directions of influx of moist air masses. The cases analysed included torrential rains with monsoon circulation from the S–SW direction at the 2–3 km high edge of the Himalayas, with special consideration to the distance from the margin of the mountains and the exposure of the slopes. They generate frequent flood waves, landslides, debris flows and upbuilding of the alluvial cones in the foreland of the mountain barriers. The impact of the orographic barrier is significantly less marked in the Polish Carpathians. In the western part, the compact edge of the Western Beskids with an altitude of 0.5–1 km and the WSW–NEE course, exposed to moist air masses inflowing from the northern sector, is fragmented eastward into smaller mountain groups, which facilitates the penetration of moist masses of air with occurrence of prolonged precipitation into the mountains. At times, the storm cloud moves along the mountain edge (the margin of the Western Bieszczady Mts.). The marginal scarp of the Foothills has a northern exposure and a height of 150–200 m, and promotes frequent convective precipitation causing local flash floods in small streams. The cases of downpours and high discharges selected for the analysis were those for which there was available a dense network of measuring stations. An insufficient number of stations in constructing precipitation maps based on interpolation would lead to distorting the spatial image. If this were the case, then the role of slope exposure, which has an essential impact on the distribution of precipitation in mountainous regions, would be completely neglected

Influence of Precipitation Forcing Uncertainty on Hydrological Simulations with the NASA South Asia Land Data Assimilation System

: Accurate meteorological estimates are critical for process-based hydrological simulation and prediction. This presents a significant challenge in mountainous Asia where in situ meteorological stations are limited and major river basins cross international borders. In this context, remotely sensed and model-derived meteorological estimates are often necessary inputs for distributed hydrological analysis. However, these datasets are difficult to evaluate on account of limited access to ground data. In this case, the implications of uncertainty associated with precipitation forcing for hydrological simulations is explored by driving the South Asia Land Data Assimilation System (South Asia LDAS) using a range of meteorological forcing products. MERRA2, GDAS, and CHIRPS produce a wide range of estimates for rainfall, which causes a widespread simulated streamflow and evapotranspiration. A combination of satellite-derived and limited in situ data are applied to evaluate model simulations and, by extension, to constrain the estimates of precipitation. The results show that available gridded precipitation estimates based on in situ data may systematically underestimate precipitation in mountainous regions and that performance of gridded satellite-derived or modeled precipitation estimates varies systematically across the region. Since no station-based data or product including station data is satisfactory everywhere, our results suggest that the evaluation of the hydrological simulation of streamflow and ET can be used as an indirect evaluation of precipitation forcing based on ground-based products or in-situ data. South Asia LDAS produces reasonable evapotranspiration and streamflow when forced with appropriate meteorological forcing and the choice of meteorological forcing should be made based on the geographical location as well as on the purpose of the simulations.

Comparative Analysis of Spatial Interpolation Techniques for Mapping Annual Mean Rainfall Estimation within a Mountainous Region

The complex topography, poor gauge representativity and uneven density make it an uphill task to accurately map precipitation in mountainous regions. This challenge was confronted with the evaluation of four different mapping techniques: Inverse Distance Weighting (IDW), Ordinary Kriging (OK), Spline and Regression Kriging (RK). An evaluation of the resulting georasters using 1) cross-validation statistics, 2) a spatial cross-consistency test and 3) a water balance analysis reveals that the techniques ignoring the information on co-variables yield the largest prediction errors. Mean error and root-mean-square error values suggested that the most biased methods were IDW and spline, with a bias almost 2 to 5 times higher than ordinary kriging. The best model accounted for mean precipitation analysis is regression Kriging, with a mean error and root mean square error values of 1.38 mm and 72.36 mm respectively, which represents 42 % less bias and 16 % higher accuracy than OK results. Comparative performances show that the regression analysis made it possible to judiciously evaluate the variable patterns and get fairly accurate values at un-gauged locations where geographical information compensated the poor availability of local data.

Modelling spatial, altitudinal and temporal variability of annual precipitation in mountainous regions: The case of the Middle Zagros, Iran

Relationships between precipitation and elevation are difficult to model for mountainous regions, due to complexities in topography and moisture sources. Attempts to model these relationships need to be tested against long-term location specific meteorological data, and hence require a case-study approach. This study uses artificial neural networks to model these relationships for the Middle of Zagros region, in semi-arid western Iran. Precipitation data for the region were collected for 1995–2007. Annual precipitation was designated as the target variable for the network, which additionally included variables significantly related to precipitation for the region, including longitude, latitude, elevation, slope, distance from the ridge, and relative distance from moisture. Long-term changes in annual precipitation for the region are investigated for 1961–2010. The artificial neural network (ANN) model explains 76% of the spatial variability of precipitation in the Middle Zagros. Precipitation predominantly increases with elevation on the windward slope, to a maximum height of 2500 m.asl, and thereafter either remains constant or decreases slowly to the ridge. Precipitation in the region has decreased significantly over the study period, with fluctuations driven by AO, NAO, ENSO and variability in the strength of pressure centers. Spectral analysis reveals significant oscillations of 2–4 and 5 yr periods, which correspond temporally with cycles in macro-scale circulation, ENSO and the Mediterranean Low pressure.

Comparison of SRM and WetSpa models efficiency for snowmelt runoff simulation

Snow melting stems from a part of precipitation in mountainous regions, which confines hydrological and hydraulic activities. Therefore, its role in runoff formation should be evaluated independently. In this paper, the performance of WetSpa and SRM models in snowmelt-runoff simulation is assessed. Aiming at this goal, 2-year regional data of Horo-Dehno watershed (263 km2) located in Iran from September 2006 to September 2008 was chosen. snowmelt-runoff model (SRM) is a conceptual physical model designed to simulate and forecast daily stream flow in mountainous watersheds where snowmelt is the main runoff factor. SRM simulates snowmelt-runoff using meteorological data and satellite images of snow cover, which, in this research, the latter is MODIS radiometer images. On the other side, water and energy transfer between soil, plants and atmosphere (WetSpa) is a distributed spatial-physical model that simulates different hydrological processes including snow melting in a cellular manner at hourly and daily time steps by employing energy balance as well as degree-day approaches. In addition, the WetSpa model for simulating different processes requires meteorological data and spatial maps. In this study, the simulation results showed the Nash–Sutcliffe efficiency of 0.54 and 0.53 for the SRM, and, 0.77 and 0.8 for the WetSpa in calibration and validation periods, respectively. Hence, in the studied area, based on the results of the qualitative (visual comparison of hydrographs as output models) and quantitative (evaluation criteria) of snowmelt-runoff simulation, the WetSpa outperformed the SRM.

Estimation of areal precipitation in the Qilian Mountains based on a gridded dataset since 1961

Based on a 0.5°×0.5° daily gridded precipitation dataset and observations in meteorological stations released by the National Meteorological Information Center, the interannual variation of areal precipitation in the Qilian Mountains during 1961–2012 is investigated using principal component analysis (PCA) and regression analysis, and the relationship between areal precipitation and drought accumulation intensity is also analyzed. The results indicate that the spatial distribution of precipitation in the Qilian Mountains can be well reflected by the gridded dataset. The gridded data-based precipitation in mountainous region is generally larger than that in plain region, and the eastern section of the mountain range usually has more precipitation than the western section. The annual mean areal precipitation in the Qilian Mountains is 724.9×108 m3, and the seasonal means in spring, summer, autumn and winter are 118.9×108 m3, 469.4×108 m3, 122.5×108 m3 and 14.1×108 m3, respectively. Summer is a season with the largest areal precipitation among the four seasons, and the proportion in summer is approximately 64.76%. The areal precipitation in summer, autumn and winter shows increasing trends, but a decreasing trend is seen in spring. Among the four seasons, summer have the largest trend magnitude of 1.7×108 m3•a–1. The correlation between areal precipitation in the mountainous region and dry-wet conditions in the mountains and the surroundings can be well exhibited. There is a negative correlation between drought accumulation intensity and the larger areal precipitation is consistent with the weaker drought intensity for this region.

Comparing Evapotranspiration from Eddy Covariance Measurements, Water Budgets, Remote Sensing, and Land Surface Models over Canadaa,b

Abstract This study compares six evapotranspiration ET products for Canada’s landmass, namely, eddy covariance EC measurements; surface water budget ET; remote sensing ET from MODIS; and land surface model (LSM) ET from the Community Land Model (CLM), the Ecological Assimilation of Land and Climate Observations (EALCO) model, and the Variable Infiltration Capacity model (VIC). The ET climatology over the Canadian landmass is characterized and the advantages and limitations of the datasets are discussed. The EC measurements have limited spatial coverage, making it difficult for model validations at the national scale. Water budget ET has the largest uncertainty because of data quality issues with precipitation in mountainous regions and in the north. MODIS ET shows relatively large uncertainty in cold seasons and sparsely vegetated regions. The LSM products cover the entire landmass and exhibit small differences in ET among them. Annual ET from the LSMs ranges from small negative values to over 600 mm across the landmass, with a countrywide average of 256 ± 15 mm. Seasonally, the countrywide average monthly ET varies from a low of about 3 mm in four winter months (November–February) to 67 ± 7 mm in July. The ET uncertainty is scale dependent. Larger regions tend to have smaller uncertainties because of the offset of positive and negative biases within the region. More observation networks and better quality controls are critical to improving ET estimates. Future techniques should also consider a hybrid approach that integrates strengths of the various ET products to help reduce uncertainties in ET estimation.

Evaluation of radar precipitation estimates near gap regions: a case study in the Colorado River basin

Radar precipitation estimation is very useful for hydrological and climatological studies. However, radar precipitation has inherent difficulty in estimating precipitation in mountainous regions. In developed countries such as the United States where there are extensive precipitation radar networks, gaps in the radar precipitation field are usually due to radar beam blockage by mountains. The goal of this study is to evaluate the performance of a daily radar precipitation field (Stage-II) against rain gauge measurements near radar gap areas in the Colorado River basin of the United States (southwestern Colorado, southeastern Utah, northeastern Arizona and northwestern New Mexico). We evaluated daily precipitation data for the years spanning from 2007 to 2009. Statistical score skills including correlation and bias are used for evaluation. Compared to gauge measurements, Stage-II fails to capture the altitude dependence of precipitation in the region. Bias analysis shows that Stage-II underestimates precipitation at higher elevation. Seasonal evaluations of Stage-II indicate that it underestimates cold season precipitation in the study area. Overall, the results show that the error in Stage-II precipitation estimates made within 100 km from the gap area, as measured against rain gauge measurements, is considerable, and caution is warranted for its use in hydrological and water management applications.

Evaluating Satellite Products for Precipitation Estimation in Mountain Regions: A Case Study for Nepal

Precipitation in mountain regions is often highly variable and poorly observed, limiting abilities to manage water resource challenges. Here, we evaluate remote sensing and ground station-based gridded precipitation products over Nepal against weather station precipitation observations on a monthly timescale. We find that the Tropical Rainfall Measuring Mission (TRMM) 3B-43 precipitation product exhibits little mean bias and reasonable skill in giving precipitation over Nepal. Compared to station observations, the TRMM precipitation product showed an overall Nash-Sutcliffe efficiency of 0.49, which is similar to the skill of the gridded station-based product Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE). The other satellite precipitation products considered (Global Satellite Mapping of Precipitation (GSMaP), the Climate Prediction Center Morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS)) were less skillful, as judged by Nash-Sutcliffe efficiency, and, on average, substantially underestimated precipitation compared to station observations, despite their, in some cases, higher nominal spatial resolution compared to TRMM. None of the products fully captured the dependence of mean precipitation on elevation seen in the station observations. Overall, the TRMM product is promising for use in water resources applications.

Assessment of RegCM4 simulated inter-annual variability and daily-scale statistics of temperature and precipitation over Mexico

The skill of a regional climate model (RegCM4) in capturing the mean patterns, interannual variability and extreme statistics of daily-scale temperature and precipitation events over Mexico is assessed through a comparison of observations and a 27-year long simulation driven by reanalyses of observations covering the Central America CORDEX domain. The analysis also includes the simulation of tropical cyclones. It is found that RegCM4 reproduces adequately the mean spatial patterns of seasonal precipitation and temperature, along with the associated interannual variability characteristics. The main model bias is an overestimation of precipitation in mountainous regions. The 5 and 95 percentiles of daily temperature, as well as the maximum dry spell length are realistically simulated. The simulated distribution of precipitation events as well as the 95 percentile of precipitation shows a wet bias in topographically complex regions. Based on a simple detection method, the model produces realistic tropical cyclone distributions even at its relatively coarse resolution (dx = 50 km), although the number of cyclone days is underestimated over the Pacific and somewhat overestimated over the Atlantic and Caribbean basins. Overall, it is assessed that the performance of RegCM4 over Mexico is of sufficient quality to study not only mean precipitation and temperature patterns, but also higher order climate statistics.