Precipitation Errors Research Articles

Improvement of land surface model simulations over India via data assimilation of satellite-based soil moisture products

Realistic representation of surface states using the land surface model (LSM) is extremely challenging owing to human-induced changes and uncertainty in forcing data. In this study, we focus on two crucial objectives pertaining to hydrology namely (a) to understand the ability of different soil moisture (SM) products to improve simulation of unmodeled irrigation processes through data assimilation process, and (b) to learn the feasibility of these SM products to correct the spatial surface soil moisture artifacts caused due to error in precipitation forcing. The utility of SM products evaluated in the present study are retrieved from different satellite sensors and algorithms such as the active satellite-based the Advanced Scatterometer (ASCAT), merged SM from European Space Agency Climate Change Initiative (ESA CCI V4.2) and the latest passive microwave-based SMOS INRA-CESBIO (SMOS-IC) SM product. The results presented for three years (2010, 2011 and 2012) suggest that assimilation of ASCAT and CCI based products effectively captures the SM changes due to irrigation. Similarly, it corrects the spatial artifacts caused due to precipitation errors. However, the single sensor based products have a limitation in spatial samples per day which is critical to capture dynamic SM products over a larger area. Hence, blended products are more effective on a larger area to capture the dynamics in a more effective manner at daily temporal resolutions.

Sensitivities of the NCEP Global Forecast System

AbstractAn important issue in developing a forecast system is its sensitivity to additional observations for improving initial conditions, to the data assimilation (DA) method used, and to improvements in the forecast model. These sensitivities are investigated here for the Global Forecast System (GFS) of the National Centers for Environmental Prediction (NCEP). Four parallel sets of 7-day ensemble forecasts were generated for 100 forecast cases in mid-January to mid-March 2016. The sets differed in their 1) inclusion or exclusion of additional observations collected over the eastern Pacific during the El Niño Rapid Response (ENRR) field campaign, 2) use of a hybrid 4D–EnVar versus a pure EnKF DA method to prepare the initial conditions, and 3) inclusion or exclusion of stochastic parameterizations in the forecast model. The Control forecast set used the ENRR observations, hybrid DA, and stochastic parameterizations. Errors of the ensemble-mean forecasts in this Control set were compared with those in the other sets, with emphasis on the upper-tropospheric geopotential heights and vorticity, midtropospheric vertical velocity, column-integrated precipitable water, near-surface air temperature, and surface precipitation. In general, the forecast errors were found to be only slightly sensitive to the additional ENRR observations, more sensitive to the DA methods, and most sensitive to the inclusion of stochastic parameterizations in the model, which reduced errors globally in all the variables considered except geopotential heights in the tropical upper troposphere. The reduction in precipitation errors, determined with respect to two independent observational datasets, was particularly striking.

The role of air-sea coupling in the downscaled hydroclimate projection over Peninsular Florida and the West Florida Shelf

A comparative analysis of two sets of downscaled simulations of the current climate and the future climate projections over Peninsular Florida (PF) and the West Florida Shelf (WFS) is presented to isolate the role of high-resolution air-sea coupling. In addition, the downscaled integrations are also compared with the much coarser, driving global model projection to examine the impact of grid resolution of the models. The WFS region is habitat for significant marine resources, which has both commercial and recreational value. Additionally, the hydroclimatic features of the WFS and PF contrast each other. For example, the seasonal cycle of surface evaporation in these two regions are opposite in phase to one another. In this study, we downscale the Community Climate System Model version 4 (CCSM4) simulations of the late twentieth century and the mid-twenty-first century (with reference concentration pathway 8.5 emission scenario) using an atmosphere only Regional Spectral Model (RSM) at 10 km grid resolution. In another set, we downscale the same set of CCSM4 simulations using the coupled RSM-Regional Ocean Model System (RSMROMS) at 10 km grid resolution. The comparison of the twentieth century simulations suggest significant changes to the SST simulation over WFS from RSMROMS relative to CCSM4, with the former reducing the systematic errors of the seasonal mean SST over all seasons except in the boreal summer season. It may be noted that owing to the coarse resolution of CCSM4, the comparatively shallow bathymetry of the WFS and the sharp coastline along PF is poorly defined, which is significantly rectified at 10 km grid spacing in RSMROMS. The seasonal hydroclimate over PF and the WFS in the twentieth century simulation show significant bias in all three models with CCSM4 showing the least for a majority of the seasons, except in the wet June-July-August (JJA) season. In the JJA season, the errors of the surface hydroclimate over PF is the least in RSMROMS. The systematic errors of surface precipitation and evaporation are more comparable between the simulations of CCSM4 and RSMROMS, while they differ the most in moisture flux convergence. However, there is considerable improvement in RSMROMS compared to RSM simulations in terms of the seasonal bias of the hydroclimate over WFS and PF in all seasons of the year. This suggests the potential rectification impact of air-sea coupling on dynamic downscaling of CCSM4 twentieth century simulations. In terms of the climate projection in the decades of 2041–2060, the RSMROMS simulation indicate significant drying of the wet season over PF compared to moderate drying in CCSM4 and insignificant changes in the RSM projection. This contrasting projection is also associated with projected warming of SSTs along the WFS in RSMROMS as opposed to warming patterns of SST that is more zonal and across the WFS in CCSM4.

Bias adjustment for decadal predictions of precipitation in Europe from CCLM

A cross-validated model output statistics (MOS) approach is applied to precipitation data from the high-resolution regional climate model CCLM for Europe. The aim is to remove systematic errors of simulated precipitation in decadal climate predictions. We developed a two-step bias-adjustment approach. In step one, we estimate model errors based on a long-term ‘CCLM assimilation run’ (regionalizing data from a global assimilation run) and observational data. In step two, the resulting transfer function is applied to the complete set of decadal hindcast simulations (285 individual runs). In contrast to lead-time-dependent bias-adjustment approaches, this one is designed for variables with poor decadal prediction skill and without dominant lead-time-dependent bias. In terms of the CCLM assimilation run, MOS is shown to be effective in predictor selection, model skill improvement, and model bias reduction. Yet, the positive effect of MOS correction is accompanied with an underestimation of precipitation variability. After MOS application, an estimated mean square skill score of more than 0.5 is observed regionally. Simulated precipitation in decadal hindcasts is further improved when the MOS is trained on the basis of other decadal hindcasts from the same regional climate model but with a large underestimation in forecast uncertainty. Our results suggest that the MOS system derived from the assimilation run is less effective but allows the potential climate predictability in decadal hindcasts and forecasts to be retained. Using hindcasts itself for training is recommended unless a statistical method is capable of distinguishing biases and predictions within a hindcasts dataset.

Assessment of the reliability of three gauged-based global gridded precipitation datasets for drought monitoring

This study evaluates the reliability on three global gridded precipitation dataset including CRU-TS-V.4.01, GPCC-V.7 and UDEL-V.4.01 to drought monitoring over Lake Urmia basin in Iran using standardised precipitation index (SPI). Assessing the precipitation data of global datasets indicate that the major error in precipitation of global datasets is bias error which means that they can recognise the behaviour of precipitation time series but there is a difference in estimation of precipitation amount. To diminish the bias error, bias correction (BC) was done before evaluating the datasets in drought monitoring. The results indicated that GPCC is the most suitable dataset over the study area for drought studies. In addition, results revealed that there is no significant difference between original and BC data in drought monitoring. For example, in nine-month SPI, the critical success index (CSI) for original CRU and CRU-BC were equal to 71 and 76%, respectively, and the GPCC and UDEL in both original and BC versions were equal to 88 and 55%, respectively. Also, it can be claim that the bias error is not important in drought studies due to the fact that during calculation of SPI, standardisation process will remove bias significantly.

Assessment of the reliability of three gauged-based global gridded precipitation datasets for drought monitoring

This study evaluates the reliability on three global gridded precipitation dataset including CRU-TS-V.4.01, GPCC-V.7 and UDEL-V.4.01 to drought monitoring over Lake Urmia basin in Iran using standardised precipitation index (SPI). Assessing the precipitation data of global datasets indicate that the major error in precipitation of global datasets is bias error which means that they can recognise the behaviour of precipitation time series but there is a difference in estimation of precipitation amount. To diminish the bias error, bias correction (BC) was done before evaluating the datasets in drought monitoring. The results indicated that GPCC is the most suitable dataset over the study area for drought studies. In addition, results revealed that there is no significant difference between original and BC data in drought monitoring. For example, in nine-month SPI, the critical success index (CSI) for original CRU and CRU-BC were equal to 71 and 76%, respectively, and the GPCC and UDEL in both original and BC versions were equal to 88 and 55%, respectively. Also, it can be claim that the bias error is not important in drought studies due to the fact that during calculation of SPI, standardisation process will remove bias significantly.

Wintertime orographic precipitation over western Tasmania

The wintertime (April - October) precipitation across western Tasmania (west of 147°E) has been studied for two years (2014 and 2015). Using the AWAP precipitation analysis, the average daily rainfall across western Tasmania was found to be 4.49 mm day-1 for all winter days and 6.99 mm day-1 for rain days (average precipitation greater than 1 mm day-1). Rain days were observed for ~63% of all days during the winter months. Rain days were frequently recorded after the pas-sage of a cold front, when winds are typically from the west and southwest, off the open Southern Ocean. The daily precipitation was found to be highly correlated (r = 0.55) with the 12 UTC ERA-Interim 1000 m wind speed at a point upwind of Tasmania, roughly 100 km off the west coast. Given the highly variable meteorology of the Southern Ocean storm track and the complex topography, western Tasmania is a natural testbed for studying orographic precipitation. Both locally blocked and unblocked flows, caused by changes in the low-level thermodynamic stability, occur frequently over the course of a winter with a stable environment having a lower average precipitation rate (3.66 mm day-1) than an unstable environment (8.40 mm day-1), although only a weak correlation (r = -0.07) was found between precipitation and Ĥ2(the square of non-dimensional mountain height). Simulated precipitation from the Australian Bureau of Meteorology’s ACCESS-VT model was found to underestimate the AWAP precipitation by ~20%. The greatest negative relative errors between the AWAP and ACCESS-VT precipitation in unblocked flow were in the lee of the mountains, over central and south-central Tasmania. For days when the flow was blocked, this region had large positive relative errors in precipitation. Over the upwind side of western Tasmania, ACCESS-VT underestimated precipitation in comparison to AWAP in both un-blocked and blocked flows. However, the network of surface sites is quite sparse over this region, which limits our confidence in both the ACCESS-VT and the AWAP precipitation products. A more detailed investigation is necessary to better appreciate limitations in the ACCESS-VT forecasts in this region.

Modelling precipitation uncertainties in a multi-objective Bayesian ecohydrological setting

Recent studies have demonstrated that hydrological model calibrations are impaired by uncertainties in observations and model structures. For a rainfall-driven model, the error in precipitation observations can lead to biased parameter estimates and predictions. For a conceptual ecohydrological model, an appropriate description of input error is essential because rainfall controls both hydrological processes and vegetation growth in the model. However, to date the impact of precipitation uncertainty on ecohydrologic model parameters and outputs has not been widely explored (Fuentes et al., 2006; Shields and Tague, 2012). The increased dimensionality of these types of models and the uncertainties associated with calibration data can make traditional approaches for characterizing precipitation errors problematic. Our study aims to investigate the impact of precipitation uncertainty for a Bayesian multi-objective calibration approach in an ecohydrological modeling study. A conceptual model that combines a hydrologic model and a modified bucket grassland model is implemented for a forested catchment in Australia. In the study, different input error descriptions are used in both single and multi-objective Bayesian calibration case studies aimed at simulating streamflow and Leaf Area Index (LAI). The emphasis on each objective is represented as different prior distributions defined for error parameters for multi-objective cases. Results show better parameter estimates and predictions for the cases including input error. Comparing the results from the cases in which different input error descriptions are used, a simple bias term works well for both streamflow and LAI estimations. Although a more complex rainfall multiplier approach to represent input error performs best for streamflow predictions, increasing the dimensionality of the input error model is not always justified given the information content of the data. In addition, some of the rainfall multipliers values are not meaningful in the real case, reflecting an overfitting problem.

Study on quantification of areal mean precipitation using satellite-gauge merging precipitation

Satellite based precipitation product (GSMaP-MVK) can be reliably used to estimate the Areal Mean Precipitation error based on “Sample Design method” (Esdd) with the effort to mitigate the problem of sparse data, especially severe in poorly gauged river basins. In addition, the satellite-gauge merging precipitation would reduce significantly the magnitude gaps between the satellite rainfall estimations and the rain gauge data. In this study, the capability of satellite-gauge merging precipitation using GSMaP-MVK and local dense rain gauge data with bias reduction approach to evaluate the AMP is investigated. The main finding is that satellite-gauge blending data which incorporates a dense rain gauge measurements shows the better capability to evaluate AMP using Esdd index than the original satellite only precipitation estimations. However, Esdd quantification performances of satellite-gauge blending precipitation are inferior to the original satellite only precipitation product GSMaP-MVK when the number of blended rain gauges is not large enough.
 Keywords: areal mean precipitation; remote sensed precipitation product; satellite-gauge merging; rainfall runoff simulations.

The northern hemisphere circumglobal teleconnection in a seasonal forecast model and its relationship to European summer forecast skill

Forecasting seasonal variations in European summer weather represents a considerable challenge. Here, we assess the performance of a seasonal forecasting model at representing a major mode of northern hemisphere summer climate variability, the circumglobal teleconnection (CGT), and the implications of errors in its representation on seasonal forecasts for the European summer (June, July, August). Using seasonal hindcasts initialised at the start of May, we find that the model skill for forecasting the interannual variability of 500 hPa geopotential height is poor, particularly over Europe and several other “centres of action” of the CGT. The model also has a weaker CGT pattern than is observed, particularly in August, when the observed CGT wavetrain is strongest. We investigate several potential causes of this poor skill. First, model variance in geopotential height in west-central Asia (an important region for the maintenance of the CGT) is lower than observed in July and August, associated with a poor representation of the link between this region and Indian monsoon precipitation. Second, analysis of the Rossby wave source shows that the source associated with monsoon heating is both too strong and displaced to the northeast in the model. This is related to errors in monsoon precipitation over the Bay of Bengal and Arabian Sea, where the model has more precipitation than is observed. Third, the model jet is systematically shifted northwards by several degrees latitude over large parts of the northern hemisphere, which may affect the propagation characteristics of Rossby waves in the model.

Evaluation of Atmospheric River Predictions by the WRF Model Using Aircraft and Regional Mesonet Observations of Orographic Precipitation and Its Forcing

Abstract Accurate forecasts of precipitation during landfalling atmospheric rivers (ARs) are critical because ARs play a large role in water supply and flooding for many regions. In this study, we have used hundreds of observations to verify global and regional model forecasts of atmospheric rivers making landfall in Northern California and offshore in the midlatitude northeast Pacific Ocean. We have characterized forecast error and the predictability limit in AR water vapor transport, static stability, onshore precipitation, and standard atmospheric fields. Analysis is also presented that apportions the role of orographic forcing and precipitation response in driving errors in forecast precipitation after AR landfall. It is found that the global model and the higher-resolution regional model reach their predictability limit in forecasting the atmospheric state during ARs at similar lead times, and both present similar and important errors in low-level water vapor flux, moist-static stability, and precipitation. However, the relative contribution of forcing and response to the incurred precipitation error is very different in the two models. It can be demonstrated using the analysis presented herein that improving water vapor transport accuracy can significantly reduce regional model precipitation errors during ARs, while the same cannot be demonstrated for the global model.

Evaluation of the ability of the Weather Research and Forecasting model to reproduce a sub-daily extreme rainfall event in Beijing, China using different domain configurations and spin-up times

Abstract. The rainfall outputs from the latest convection-scale Weather Research and Forecasting (WRF) model are shown to provide an effective means of extending prediction lead times in flood forecasting. In this study, the performance of the WRF model in simulating a regional sub-daily extreme rainfall event centred over Beijing, China is evaluated at high temporal (sub-daily) and spatial (convective-resolving) scales using different domain configurations and spin-up times. Seven objective verification metrics that are calculated against the gridded ground observations and the ERA-Interim reanalysis are analysed jointly using subjective verification methods to identify the likely best WRF configurations. The rainfall simulations are found to be highly sensitive to the choice of domain size and spin-up time at the convective scale. A model run covering northern China with a 1 : 5 : 5 horizontal downscaling ratio (1.62 km), 57 vertical layers (less than 0.5 km), and a 60 h spin-up time exhibits the best performance in terms of the accuracy of rainfall intensity and the spatial correlation coefficient (R′). A comparison of the optimal run and the initial run performed using the most common settings reveals clear improvements in the verification metrics. Specifically, R′ increases from 0.226 to 0.67, the relative error of the maximum precipitation at a point rises from −56 to −11.7 %, and the root mean squared error decreases by 33.65 %. In summary, re-evaluation of the domain configuration options and spin-up times used in WRF is crucial for improving the accuracy and reliability of rainfall outputs used in applications related to regional sub-daily heavy rainfall (SDHR).

Spatial downscaling of TRMM precipitation data considering the impacts of macro-geographical factors and local elevation in the Three-River Headwaters Region

Precipitation products with high spatial resolution are important for basin-scale hydrological and meteorological applications. Downscaling techniques commonly used with satellite-derived rainfall data build statistical regression relationships between the precipitation and land surface characteristics to obtain rainfall estimates with improved spatial resolution. However, these relationships tend to be extended mistakenly from the regional scale to the hill slope scale. This paper introduces a quadratic parabolic profile (QPP) model for downscaling precipitation. The proposed technique uses a quadratic parabolic equation to express the rule for changes of precipitation with elevation. It is assumed that precipitation is the primary factor restricting vegetation growth during the growing season. Therefore, an ordinary least square regression method is used to fit an “elevation–normalized difference vegetation index (NDVI)” function to determine the parameters of the QPP model. This method was implemented in the Three-River Headwaters Region (TRHR) during the growing seasons of 2009–2013 for both monthly and total precipitation. The results indicated that the precipitation estimates downscaled using the QPP method had higher accuracies than those of commonly used exponential regression, multiple linear regression, and geographically weighted regression models. The average root mean square errors (RMSEs) and mean absolute percent errors (MAPEs) of total precipitation during the growing season of the commonly used models were 17%–69% and 17%–92% higher, respectively, than those of the QPP model. Meanwhile, the precipitation downscaled using the QPP technique also had lower MAPEs and RMSEs than the PERSIANN-CCS, PERSIANN-CDR, GSMaP-RNL, and GSMaP-RNLG products. Downscaled precipitation estimates from the QPP model exhibited patterns with elevation that were more detailed and more reliable than from the commonly used downscaling methods and another four satellite products. In addition, the QPP model is insensitive to errors in the NDVI or elevation. These findings suggest the proposed approach could be implemented successfully to downscale both monthly and total precipitation of the Tropical Rainfall Measuring Mission (TRMM) 3B43 product throughout the growing season in the TRHR.

A water balance based, spatiotemporal evaluation of terrestrial evapotranspiration products across the contiguous United States.

Accurate gridded estimates of evapotranspiration (ET) are essential to the analysis of terrestrial water budgets. In this study, ET estimates from three gridded energy-balance based products (ETEB) with independent model formations and data forcings are evaluated for their ability to capture long term climatology and inter-annual variability in ET derived from a terrestrial water budget (ETWB) for 671 gaged basins across the CONUS. All three ETEB products have low spatial bias and accurately capture inter-annual variability of ETWB in the central US, where ETEB and ancillary estimates of change in total surface water storage (ΔTWS) from the GRACE satellite project appear to close terrestrial water budgets. In humid regions, ETEB products exhibit higher long-term bias, and the covariability of ETEB and ETWB decreases significantly. Several factors related to either failure of ETWB, such as errors in ΔTWS and precipitation, or failure of ETEB, such as treatment of snowfall and horizontal heat advection, explain some of these discrepancies. These results mirror and build on conclusions from other studies: on inter-annual timescales, ΔTWS and error in precipitation estimates are non-negligible uncertainties in ET estimates based on a terrestrial water budget, and this confounds their comparison to energy balance ET models. However, there is also evidence that in at least some regions, climate and landscape features may also influence the accuracy and long-term bias of ET estimates from energy balance models, and these potential errors should be considered when using these gridded products in hydrologic applications.

How Do Precipitation Error Characteristics Affect The Simulation Of Streamflow?

How Do Precipitation Error Characteristics Affect The Simulation Of Streamflow?

Precipitation From a Multiyear Database of Convection‐Allowing WRF Simulations

AbstractConvection‐allowing models (CAMs) have become frequently used for operational forecasting and, more recently, have been utilized for general circulation model downscaling. CAM forecasts have typically been analyzed for a few case studies or over short time periods, but this limits the ability to judge the overall skill of deterministic simulations. Analysis over long time periods can yield a better understanding of systematic model error. Four years of warm season (April–August, 2010–2013)‐simulated precipitation has been accumulated from two Weather Research and Forecasting (WRF) models with 4 km grid spacing. The simulations were provided by the National Center for Environmental Prediction (NCEP) and the National Severe Storms Laboratory (NSSL), each with different dynamic cores and parameterization schemes. These simulations are evaluated against the NCEP Stage‐IV precipitation data set with similar 4 km grid spacing. The spatial distribution and diurnal cycle of precipitation in the central United States are analyzed using Hovmöller diagrams, grid point correlations, and traditional verification skill scoring (i.e., ETS; Equitable Threat Score). Although NCEP‐WRF had a high positive error in total precipitation, spatial characteristics were similar to observations. For example, the spatial distribution of NCEP‐WRF precipitation correlated better than NSSL‐WRF for the Northern Plains. Hovmöller results exposed a delay in initiation and decay of diurnal precipitation by NCEP‐WRF while both models had difficulty in reproducing the timing and location of propagating precipitation. ETS was highest for NSSL‐WRF in all domains at all times. ETS was also higher in areas of propagating precipitation compared to areas of unorganized diurnal scattered precipitation. Monthly analysis identified unique differences between the two models in their abilities to correctly simulate the spatial distribution and zonal motion of precipitation through the warm season.

Assessing the Skill of Convection-Allowing Ensemble Forecasts of Precipitation by Optimization of Spatial-Temporal Neighborhoods

The current neighborhood probability (NP) method mainly considers the spatial displacement error in high-resolution precipitation forecasts, but the problem of the forecast time exceeding or lagging behind the observed field has not been properly solved. Therefore, a temporal factor was introduced into the NP method in this paper, and precipitation forecasts were evaluated in different spatial-temporal neighborhoods based on the improved NP method and fractions skill score (FSS), combined with the relative operating characteristic (ROC) curve. The results indicated that the forecasting accuracy of the ensemble forecast was higher than the control forecast. The neighborhood ensemble probability (NEP) and probability matched mean (PMM) methods were superior to the traditional ensemble mean (EM) method in forecasting heavy rainfall, which compensated for the limitations of the heavy rainfall forecasting ability of EM. For such squall line processes, a spatial scale of 15–45 km neighborhood radius could effectively rectify the displacement error of precipitation. There was a corresponding relationship between temporal scale and rainfall intensity in convective-scale precipitation forecast, so the temporal uncertainty of different levels of precipitation could be captured by different temporal scales. The spatial and temporal scales had interdependent influences on precipitation forecast effects, which could be affected by the intrinsic spatial-temporal scale of convective-scale weather systems. The improved NP method could simultaneously reflect the spatial and temporal uncertainties of convective-scale precipitation forecasts in high-resolution models, achieving a comprehensive assessment of spatial-temporal scale and providing probabilistic forecast results that match different levels of precipitation.

Wintertime orographic precipitation over western Tasmania

The wintertime (April - October) precipitation across western Tasmania (west of 147°E) has been studied for two years (2014 and 2015). Using the AWAP precipitation analysis, the average daily rainfall across western Tasmania was found to be 4.49 mm day-1 for all winter days and 6.99 mm day-1 for rain days (average precipitation greater than 1 mm day-1). Rain days were observed for ~63% of all days during the winter months. Rain days were frequently recorded after the pas-sage of a cold front, when winds are typically from the west and southwest, off the open Southern Ocean. The daily precipitation was found to be highly correlated (r = 0.55) with the 12 UTC ERA-Interim 1000 m wind speed at a point upwind of Tasmania, roughly 100 km off the west coast.Given the highly variable meteorology of the Southern Ocean storm track and the complex topography, western Tasmania is a natural testbed for studying orographic precipitation. Both locally blocked and unblocked flows, caused by changes in the low-level thermodynamic stability, occur frequently over the course of a winter with a stable environment having a lower average precipitation rate (3.66 mm day-1) than an unstable environment (8.40 mm day-1), although only a weak correlation (r = -0.07) was found between precipitation and Ĥ2(the square of non-dimensional mountain height).Simulated precipitation from the Australian Bureau of Meteorology’s ACCESS-VT model was found to underestimate the AWAP precipitation by ~20%. The greatest negative relative errors between the AWAP and ACCESS-VT precipitation in unblocked flow were in the lee of the mountains, over central and south-central Tasmania. For days when the flow was blocked, this region had large positive relative errors in precipitation. Over the upwind side of western Tasmania, ACCESS-VT underestimated precipitation in comparison to AWAP in both un-blocked and blocked flows. However, the network of surface sites is quite sparse over this region, which limits our confidence in both the ACCESS-VT and the AWAP precipitation products. A more detailed investigation is necessary to better appreciate limitations in the ACCESS-VT forecasts in this region.

Sensitivity of Indian summer monsoon simulation to physical parameterization schemes in the WRF model

A set of 17 experiments, using various combinations of physical parameterization schemes in the Weather Research and Forecasting (WRF) model, were carried out to choose a combination suitable for simulating the Indian summer monsoon. The model experiments, forced with the ERA-Interim reanalysis data, were at 30 km horizontal resolution. The WRF model experiments were initialized on 1 May of each year and integrated until 30 September to cover the entire monsoon season for the years 1982 to 2013. The results indicate that the simulated Indian summer monsoon precipitation and 2 m air temperature are sensitive to the physical parameterization schemes in the WRF model and that choosing the correct combination of physical parameterization schemes is essential for simulating the Indian summer monsoon realistically. Our analysis shows that a model setup with the Kain-Fritsch cumulus scheme, a radiation package with the Dudhia shortwave and Rapid Radiative Transfer Model longwave schemes, the Yonsei State University planetary boundary layer scheme, the WRF Single-Moment 3-class microphysics scheme, the revised MM5 Monin-Obukhov surface layer scheme, and the Unified Noah land surface model is suitable for simulating the precipitation realistically. The model setup with a combination of these physical parameterization schemes was found to have smaller biases and root mean square errors in the simulated precipitation, along with a realistic simulation of intraseasonal and interannual variability of precipitation. The results of this study will be useful to researchers and forecasters using the WRF model to improve the Indian summer monsoon simulations/forecasts over the Indian region.

Changes in Extratropical Cyclone Precipitation and Associated Processes during the Twenty-First Century over Eastern North America and the Western Atlantic Using a Cyclone-Relative Approach

This study investigates the future change in precipitation associated with extratropical cyclones over eastern North America and the western Atlantic during the cool season (November–March) through the twenty-first century. A cyclone-relative approach is applied to 10 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) in order to isolate precipitation changes for different cyclone intensities and storm life cycle, as well as determine the relevant physical processes associated with these changes. The historical analysis suggests that models with better performance in predicting extratropical cyclones tend to have smaller precipitation errors, and the ensemble mean has a smaller mean absolute error than the individual models. By the late-twenty-first century, the precipitation amount associated with cyclones increases by 5%–25% over the U.S. East Coast, with about 90% of the increase from the relatively strong (<990 hPa) and moderate (990–1005 hPa) cyclones. Meanwhile, the precipitation rate increases by 15%–25% over the U.S. East Coast for the strong cyclone centers, which is larger than the moderate and weak cyclones. The relatively strong cyclones just inland of the U.S. East Coast have the largest increase (~30%) in precipitation rate, since these centers over land have the largest increase in low-level temperature (and moisture), a decrease (5%–13%) in the static stability, and an increase (~5%) in upward motion during the late-twenty-first century. This east coast region also has an increase in cyclone intensity in the future even though there is a decrease in low-level baroclinicity, which suggests that the latent heat release from heavier precipitation contributes to this storm deepening.