Frequency Distribution Of Precipitation Research Articles

The response of soil carbon mineralization losses to changes in rainfall frequency is seasonally dependent in an estuarine saltmarsh

Altered rainfall distribution patterns resulting from climate change have substantial effects on soil carbon (C) cycling in terrestrial ecosystems particularly in water-limited regions. However, how rainfall redistribution affects soil C mineralization (CO2 and CH4 fluxes) in humid regions such as of the coastal saltmarshes remain unclear. We conducted mesocosm experiments in an estuarine saltmarsh in the Yellow River Delta of China, where we simulated three rainfall frequency scenarios (high-frequency, medium-frequency and low-frequency) with the same total rainfall amount in the dry and wet seasons, respectively. Soil CO2 and CH4 fluxes were measured before and after rain frequency treatment during a 40-day period for each season. The decrease in rainfall frequency significantly reduced the mean soil CO2 and CH4 fluxes during the dry season, but had no effect on either flux during the wet season. The seasonal variation in the response of soil C mineralization to rainfall frequency changes could be explained by the changes in antecedent soil water and salinity conditions, soil C substrate, microbial activities and diversity. Thus, the effects of changes in rainfall frequency on soil C mineralization are regulated by season, and should be considered when predicting the future C balance of coastal wetland ecosystems. Furthermore, the shift in precipitation frequency distribution towards increasing heavy rainfall events during the dry season in this region will have a great effect on soil C losses, potentially feeding back into the soil C budget and stability in this estuarine saltmarsh.

Bimodality in simulated precipitation frequency distributions and its relationship with convective parameterizations

Bimodality in precipitation frequency distributions is often evident in atmospheric models, but rarely in observations. This study i) proposes a metric to objectively quantify the bimodality in precipitation distributions, ii) evaluates model simulations contributed to the Coupled Model Intercomparison Project (CMIP) phase 5 (CMIP5), phase 6 (CMIP6), and the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains (DYAMOND) project by comparing them to satellite-based and reanalysis precipitation products, and iii) investigates possible origins of bimodal precipitation distributions. Our results reveal that about 83% (20 out of 24) of CMIP5 and 70% (21 out of 30) of CMIP6 models used in this study exhibit bimodal distributions. The few DYAMOND models that use a deep convective parameterization also show bimodal distributions, while most DYAMOND models do not. Predictably, the bimodality originates from the separation of precipitation process between resolved grid-scale and parameterized subgrid-scale. However, in a larger number of models bimodality arises from the parameterized subgrid-scale convective precipitation alone.

Reproducibility of Equatorial Kelvin Waves in a Superparameterized MIROC: 1. Implementation and Verification of Blockwise‐Coupled SP‐MIROC

AbstractThe potential scope of superparameterization (SP) was extended to higher resolutions of the global climate model (GCM) component by devising a technique called blockwise coupling. In this method, a horizontal average of multiple GCM columns, instead of one, is coupled to a cloud‐resolving model (CRM) domain. This enables SP‐GCMs to reduce the computational cost drastically, enabling higher‐resolution GCMs to be superparameterized. A blockwise‐coupled SP‐GCM called SP‐MIROC was implemented by coupling the climate model MIROC6 to the CRM SCALE‐RM. The 4 × 4‐bundled SP‐MIROC successfully reproduced horizontal patterns and frequency distributions of precipitation and realistic amplitudes of equatorial Kelvin waves (EKWs), which were underestimated in the standard MIROC6. As discussed in Yamazaki and Miura (2024b, https://doi.org/10.1029/2023MS003837) of this study, the amplitude boost of EKWs was enabled by a top‐heavy heating in SP‐MIROC. Comparison of power spectra between the 4 × 4‐bundled SP‐MIROC and nonbundled SP‐MIROC indicated that the effective resolution of dynamic variables was not degraded by the blockwise technique. Rather, spectra in the 4 × 4‐bundled SP‐MIROC were more realistic than those in the nonbundled SP‐MIROC. Although the 4 × 4‐bundling limits convective coupling in the smallest GCM scale, it could offer the best match of resolutions between the GCM‐handled dynamics and SP‐derived physics because the effective resolution of the dynamics is lower than the nominal grid spacing.

Daily Precipitation Frequency Distributions Impacts on Land-Surface Simulations of CONUS

Many precipitation-driven data products from land data assimilation systems support assessments of droughts, floods, and other societally-relevant land-surface processes. The accumulated precipitation used as input to these products has a significant impact on water budgets; however, the effects of daily distribution of precipitation on these products are not well known. A comparison of the Integrated Multi-satellite Retrievals for GPM (IMERG) and Climate Hazards Group InfraRed Precipitation with Stations version 2 (CHIRPS2) rainfall products over the continental United States (CONUS) was performed to quantify the impacts of the daily distribution of precipitation on biases and errors in soil moisture, runoff, and evapotranspiration (ET). Since the total accumulated precipitation between the IMERG and CHIRPS product differed, a third precipitation product, CHIRPS-to-IMERG (CHtoIM), was produced that used CHIRPS2 accumulated precipitation totals and the daily precipitation frequency distribution of IMERG. This new product supported a controlled analysis of the impact of precipitation frequency distribution on simulated hydrological fields. The CHtoIM had higher occurrences of precipitation in the 0–5 mm day−1 range, with a lower occurrence of dry days, which decreased soil moisture and surface runoff in the land-surface model. The surface soil layer had a tendency to reach saturation more often in the CHIRPS2 simulations, where the number of moderate to heavy precipitation days (>5 mm day−1) was increased. Using the blended CHtoIM product as input reduced errors in surface soil moisture by 5–15% when compared to Soil Moisture Active/Passive (SMAP) data. Similarly, ET errors were also slightly decreased (~2%) when compared to SSEBop data. Moderate changes in daily precipitation distributions had a quantifiable impact on soil moisture, runoff, and ET. These changes usually improved the model when compared to other modeled and observational datasets, but the magnitude of the improvements varied by region and time of year.

A Model Framework for Stochastic Representation of Uncertainties Associated with Physical Processes in NOAA’s Next Generation Global Prediction System (NGGPS)

Abstract In this study, we propose a physical-process-based stochastic parameterization scheme using cellular automata for NOAA’s Next Generation Global Prediction System. The cellular automata, used to simulate stochastic processes such as the production and destruction of subgrid convective elements, are conditioned on unresolved vertical motion that follows a prescribed stochastically generated skewed distribution (SGS). The SGS is described by a stochastic differential equation and linked to observations by taking into account the first four moments from an observed dataset. In the proposed parameterization framework, we emphasize the need for a dynamical memory term to be included in physical-process-based stochastic parameterizations, and we illustrate the requirement for the dynamical memory using the Mori–Zwanzig formalism. Although this paper focuses on the methodology, early results indicate that if we apply our stochastic framework to deep cumulus convection, it is found that the frequency distribution of precipitation is improved in a single-member stochastic forecast, and some improved spread–skill relationship in ensemble runs can be found in state variables in the tropics, as well as in the subtropics.

Design water demand of irrigation for a large region using a high-dimensional Gaussian copula

Abstract. Spatial and frequency distributions of precipitation should be considered in determining design water demand of irrigation for a large region. In Guangdong Province, South China, as a study case, an eight-dimensional joint distribution of precipitation for agricultural sub-regions was developed. A design procedure for water demand of irrigation for a given frequency of precipitation of the entire region was proposed. Water demands of irrigation in the entire region and its sub-regions using three design methods, i.e., equalized frequency (EF), typical year (TY) and most-likely weight function (MLW), were compared. Results demonstrated that the Gaussian copula efficiently fitted the high-dimensional joint distribution of eight sub-regional precipitation values. The Kendall frequency was better than the conventional joint frequency to analyze the linkage between the frequency of precipitation of the entire region and individual sub-regions. For given frequencies of precipitation of the entire region, design water demands of irrigation of the entire region among the MLW, EF and TY methods slightly differed, but those of individual sub-regions of the MLW and TY methods fluctuated around the demand lines of the EF method. The alterations of design water demand in sub-regions were more complicated than those in the entire region. The design procedure using the MLW method in association with a high-dimensional copula, which simulated individual univariate distributions, captured their dependences for multi-variables, and built a linkage between regional frequency and sub-regional frequency of precipitation, is recommended for design water demand of irrigation for a large region.

Copula-based composite likelihood approach for frequency analysis of short annual precipitation records

Abstract Hydrological series lengths are decreasing due to decreasing investments and increasing human activities. For short sequences, a copula-based composite likelihood approach (CBCLA) has been employed to enhance the quality of hydrological design values. However, the Pearson type III (P-III) distribution for short annual precipitation records has not yet been thoroughly investigated using the CBCLA. This study used the CBCLA to incorporate the concurrent and non-concurrent periods contained in data of various lengths into an integrated framework to estimate the parameters of precipitation frequency distributions. The marginal distributions were fitted using the P-III distribution, and the joint probability was constructed using a copula which offers flexibility in choosing arbitrary marginals and dependence structure. Furthermore, the uncertainties in the estimated precipitation design values for the short series obtained from this approach were compared with those obtained from univariate analysis. Then, Monte-Carlo simulations were performed to examine the feasibility of this approach. The annual precipitation series at four stations in Weihe River basin, China, were used as a case study. Results showed that CBCLA with P-III marginals reduced the uncertainty in the precipitation design values for the short series and the reduction in the uncertainty became more significant with longer adjacent series.

Downscaling Extreme Precipitation from CMIP5 Simulations Using Historical Analogs

AbstractAn approach for downscaling daily precipitation extremes using historical analogs is applied to simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5). The method employs a multistep procedure in which the occurrence of extreme precipitation on a given target day is determined on the basis of the probability of extreme precipitation on that day’s closest historical analogs. If extreme precipitation is expected, daily precipitation observations associated with the historical analogs are used to approximate precipitation amounts on the target day. By applying the analog method to historical simulations, the ability of the CMIP5 models to simulate synoptic weather patterns associated with extreme precipitation is assessed. Differences between downscaled and observed precipitation extremes are investigated by comparing the precipitation frequency distributions for a subset of rarely selected extreme analog days with those for all observed days with extreme precipitation. A supplemental composite analysis of the synoptic weather patterns on these rarely selected analog days is utilized to elucidate the meteorological factors that contribute to such discrepancies. Overall, the analog method as applied to CMIP5 simulations yields realistic estimates of historical precipitation extremes, with return-period precipitation biases that are comparable in magnitude to those obtained from dynamically downscaled simulations. The analysis of rarely selected analog days reveals that precipitation amounts on these days are generally larger than precipitation amounts on all days with extreme precipitation, leading to an underestimation of return-period precipitation amounts at many stations. Furthermore, the synoptic composite analysis reveals that tropical cyclones are a common feature on these rarely selected analog days.

Evaluation of precipitation over an oceanic region of Japan in convection-permitting regional climate model simulations

We investigated the performance of a convection-permitting regional climate model with respect to precipitation in the present climate around the southwestern oceanic region of Japan. The effects of explicit representation of convective processes without cumulus parameterization can be properly estimated by using a model domain without complex topography or convoluted coastlines. The amounts of annual and monthly precipitation and the frequencies of daily and hourly precipitation were well reproduced by the convection-permitting model with a 2-km grid spacing, and its performance was better than that of a model with a coarser mesh. In particular, the frequencies of hourly precipitation in the convection-permitting simulation matched the observed frequencies for precipitation intensities below 20 mm h−1. Above intensities of 20 mm h−1, however, the convection-permitting model tended to overestimate the frequency of hourly precipitation. To explore the mechanism of this overestimation of heavy hourly precipitation, the sensitivity of the frequency distribution of precipitation to the horizontal resolution was tested by changing the horizontal grid spacing of the model from 2 to 4 km and then 1.5 km. The results showed that the overestimation was increased when the horizontal resolution was coarser, owing to spurious grid-scale precipitation, which causes heavy precipitation to be highly concentrated in a single grid. This spurious grid-scale precipitation may be caused by insufficient representation of convective downdrafts in convection-permitting simulations by models with coarser resolutions.

Assessment of the performance of CORDEX-South Asia experiments for monsoonal precipitation over the Himalayan region during present climate: part I

Analysis of regional climate simulations to evaluate the ability of 11 Coordinated Regional Climate Downscaling Experiment in South Asia experiments (CORDEX-South Asia) along with their ensemble to produce precipitation from June to September (JJAS) over the Himalayan region have been carried out. These suite of 11 combinations come from 6 regional climate models (RCMs) driven with 10 initial and boundary conditions from different global climate models and are collectively referred here as 11 CORDEX South Asia experiments. All the RCMs use a similar domain and are having similar spatial resolution of 0.44° (~50 km). The set of experiments are considered to study precipitation sensitivity associated with the Indian summer monsoon (ISM) over the study region. This effort is made as ISM plays a vital role in summertime precipitation over the Himalayan region which acts as driver for the sustenance of habitat, population, crop, glacier, hydrology etc. In addition, so far the summer monsoon precipitation climatology over the Himalayan region has not been studied with the help of CORDEX data. Thus this study is initiated to evaluate the ability of the experiments and their ensemble in reproducing the characteristics of summer monsoon precipitation over Himalayan region, for the present climate (1970–2005). The precipitation climatology, annual precipitation cycles and interannual variabilities from each simulation have been assessed against the gridded observational dataset: Asian Precipitation-Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources for the given time period. Further, after the selection of the better performing experiment the frequency distribution of precipitation was also studied. In this study, an approach has also been made to study the degree of agreement among individual experiments as a way to quantify the uncertainty among them. The experiments though show a wide variation among themselves and individually over time and space in simulating precipitation distribution over the study region, but noticeably along the foothills of the Himalayas all the simulations show dry precipitation bias against the corresponding observation. In addition, as we move towards higher elevation regions these experiments in general show wet bias. The experiment driven by EC-EARTH global climate model and downscaled using Rossby Center regional Atmospheric model version 4 developed by Swedish Meteorological and Hydrological Institute (SMHI-RCA4) simulate precipitation closely in correspondence with the observation. The ensemble outperforms the result of individual experiments. Correspondingly, different kinds of statistical analysis like spatial and temporal correlation, Taylor diagram, frequency distribution and scatter plot have been performed to compare the model output with observation and to explain the associated resemblance, robustness and dynamics statistically. Through the bias and ensemble spread analysis, an estimation of the uncertainty of the model fields and the degree of agreement among them has also been carried out in this study. Overview of the study suggests that these experiments facilitate precipitation evolution and structure over the Himalayan region with certain degree of uncertainty.

The transformation of frequency distributions of winter precipitation to spring streamflow probabilities in cold regions; case studies from the Canadian Prairies

Hydrological processes alter the states and/or locations of water, and so they can be regarded as being transformations of the properties of the time series of input variables to those of output variables, such as the transformation of precipitation to streamflow. Semi-arid cold regions such as the Canadian Prairies have extremely low annual streamflow efficiencies because of high infiltration rates, large surface water storage capacities, high evaporation rates and strong climate seasonality. As a result snowfall produces the majority of streamflow. It is demonstrated that the probability distributions of Prairie spring streamflows are controlled by three frequency transformations. The first is the transformation of snowfall by wind redistribution and ablation over the winter to form the spring snowpack. The second transformation is the melt of the spring snowpack to produce runoff over frozen agricultural soils. The third is the transformation of runoff to streamflow by the filling and spilling of depressional storage by connecting fields, ponds, wetlands and lakes. Each transformation of the PDF of the input variable to that of the output variable is demonstrated at a number of locations in the Canadian Prairies and is explained in terms of the hydrological processes causing the transformation. The resulting distributions are highly modified from that of precipitation, and the modification depends on which processes dominate streamflow formation in each basin. The results demonstrate the need to consider the effect of the interplay among hydrological processes, climate and basin characteristics in transforming precipitation frequency distributions into those of streamflow for the design of infrastructure and for water management.

Evaluation of daily precipitation statistics and monsoon onset/retreat over western Sahel in multiple data sets

The West Africa rainfall regime constitutes a considerable challenge for Regional Climate Models (RCMs) due to the complexity of dynamical and physical processes that characterise the West African Monsoon. In this paper, daily precipitation statistics are evaluated from the contributions to the AFRICA-CORDEX experiment from two ERA-Interim driven Canadian RCMs: CanRCM4, developed at the Canadian Centre for Climate Modelling and Analysis (CCCma) and CRCM5, developed at the University of Quebec at Montreal. These modelled precipitation statistics are evaluated against three gridded observed datasets—the Global Precipitation Climatology Project (GPCP), the Tropical Rainfall Measuring Mission (TRMM), and the Africa Rainfall Climatology (ARC2)—and four reanalysis products (ECMWF ERA-Interim, NCEP/DOE Reanalysis II, NASA MERRA and NOAA-CIRES Twentieth Century Reanalysis). The two RCMs share the same dynamics from the Environment Canada GEM forecast model, but have two different physics’ packages: CanRCM4 obtains its physics from CCCma’s global atmospheric model (CanAM4), while CRCM5 shares a number of its physics modules with the limited-area version of GEM forecast model. The evaluation is focused on various daily precipitation statistics (maximum number of consecutive wet days, number of moderate and very heavy precipitation events, precipitation frequency distribution) and on the monsoon onset and retreat over the Sahel region. We find that the CRCM5 has a good representation of daily precipitation statistics over the southern Sahel, with spatial distributions close to GPCP dataset. Some differences are observed in the northern part of the Sahel, where the model is characterised by a dry bias. CanRCM4 and the ERA-Interim and MERRA reanalysis products overestimate the number of wet days over Sahel with a shift in the frequency distribution toward smaller daily precipitation amounts than in observations. Both RCMs and reanalyses have difficulties in reproducing the local onset date over the Sahel region. Nevertheless, the large-scale features of the monsoon precipitation evolution over West Africa are well reproduced by the RCMs, whereas the northern limit of the rainy bands is less accurately reproduced. Both RCMs exhibit an overall good representation of the local retreat index over the Sahel region.

Development of sampling downscaling: a case for wintertime precipitation in Hokkaido

This study has developed sampling downscaling (SmDS), in which dynamical downscaling (DDS) is executed for a few of period selected from a long-term integration by general circulation model based on an observed statistical relationship between large-scale climate and regional-scale precipitation. SmDS expectedly produces climatology and frequency distribution of precipitation over a nested region with reducing computational cost, if a global-scale climate pattern mostly controls regional-scale weather statistics. Here SmDS was attempted for wintertime precipitation over Hokkaido, Japan, because a linkage between snowfall and sea-level pressure patterns has been known by Japanese synopticians and it can be detected by singular value decomposition (SVD) analysis on wintertime inter-annual variability during the period from 1980/1981 to 2009/2010 for precipitation over Hokkaido and moisture flux convergence around there. DDS for the full period over the same domain was also performed for comparison with SmDS. SmDS selected two winters from the top and two winters from the bottom of the projection onto the first SVD mode. It was found that, comparing with the full DDS, SmDS indeed provided unbiased statistics for average but exaggerated extreme statistics such as heavy rainfall frequency. It was also shown that the sampling in the SmDS method was much more effective than the random sampling.

Evaluation of the Eta Simulations Nested in Three Global Climate Models

To provide long-term simulations of climate change at higher resolution, Regional Climate Models (RCMs) are nested in global climate models (GCMs). The objective of this work is to evaluate the Eta RCM simulations driven by three global models, the HadGEM2-ES, BESM, and MIROC5, for the present period, 1961-1990. The RCM domain covers South America, Central America, and Caribbean. These simulations will be used for assessment of climate change projections in the region. Maximum temperatures are generally underestimated in the domain, in particular by MIROC5 driven simulations, in summer and winter seasons. Larger spread among the simulations was found in the minimum temperatures, which showed mixed signs of errors. The spatial correlations of temperature simulations against the CRU observations show better agreement for the MIROC5 driven simulations. The nested simulations underestimate precipitation in large areas over the continent in austral summer, whereas in winter overestimate occurs in southern Amazonia, and underestimate in southern Brazil and eastern coast of Northeast Brazil. The annual cycle of the near-surface temperature is underestimated in all model simulations, in all regions in Brazil, and in most of the year. The temperature and precipitation frequency distributions reveal that the RCM and GCM simulations contain more extreme values than the CRU observations. Evaluations of the climatic extreme indicators show that in general hot days, warm nights, and heat waves are increasing in the period, in agreement with observations. The Eta simulations driven by HadGEM2-ES show wet trends in the period, whereas the Eta driven by BESM and by MIROC5 show trends for drier conditions.

Frequency Analysis for Precipitation Events and Dry Durations of Virginia

Stormwater Control Measures (SCMs) are widely used to control and treat stormwater runoff pollution. The first step in SCM design is to evaluate the precipitation patterns at a site. SCMs are normally designed using a storm with a specific return period. A robust design process that uses frequency distribution of precipitation and monitoring of performance could improve our understanding of the behavior and limitations of a particular design. This is not the current norm in the design of SCMs. In this research, frequency analyses (FA) of precipitation events was conducted using hourly precipitation data from 1948 to 2010 for eight sites representing the four major physiographic regions of Virginia. The available data were treated using an inverse distance method to eliminate missing gaps before processing into events determined by minimum inter-event times. FA was at each site to develop frequency plots of precipitation and dry duration. FA of the eight locations indicates a range of rainfall depths from 22.9 mm in Bristol to 35.6 mm in Montebello, compared to the nominal “water quality storm” of 25.4 mm (i.e., 1 in.). Similarly, for dry duration, for a 10 % exceedance probability, the range is from 16.8 days in Richmond and Norfolk to 19.5 days in Montebello. Dry duration provides guidance for vegetation selection, which is important for some SCMs. The degree of variability in both parameters argues for consideration of site-specific information in design. FA was also used to provide guidance to improve monitoring programs. Monte Carlo simulations demonstrated that performance monitoring programs applied in different regions would likely encounter more than 30 % of precipitation events less than 6.35 mm, and 10 % over 25.4 mm under various sampling regimes. The percentages of precipitation events encountered in the Coastal Plain and Piedmont regions are not impacted by sampling regimes, however the Blue Ridge Mountains and Valley and Ridge regions are likely impacted. Anticipating event occurrences improves the chances of implementing a successful monitoring program. The use of these results could enhance the performance of SCMs with consideration of local conditions for both monitoring SCMs and their design basis.

Precipitation Partitioning, Tropical Clouds, and Intraseasonal Variability in GFDL AM2

AbstractA set of Geophysical Fluid Dynamics Laboratory (GFDL) Atmospheric Model version 2 (AM2) sensitivity simulations by varying an entrainment threshold rate to control deep convection occurrence are used to investigate how cumulus parameterization impacts tropical cloud and precipitation characteristics. In the tropics, model convective precipitation (CP) is frequent and light, while large-scale precipitation (LSP) is intermittent and strong. With deep convection inhibited, CP decreases significantly over land and LSP increases prominently over ocean. This results in an overall redistribution of precipitation from land to ocean. A composite analysis reveals that cloud fraction (low and middle) and cloud condensate associated with LSP are substantially larger than those associated with CP. With about the same total precipitation and precipitation frequency distribution over the tropics, simulations having greater LSP fraction tend to have larger cloud condensate and low and middle cloud fraction.Simulations having a greater LSP fraction tend to be drier and colder in the upper troposphere. The induced unstable stratification supports strong transient wind perturbations and LSP. Greater LSP also contributes to greater intraseasonal (20–100 days) precipitation variability. Model LSP has a close connection to the low-level convergence via the resolved grid-scale dynamics and, thus, a close coupling with the surface heat flux. Such wind–evaporation feedback is essential to the development and maintenance of LSP and enhances model precipitation variability. LSP has stronger dependence and sensitivity on column moisture than CP. The moisture–convection feedback, critical to tropical intraseasonal variability, is enhanced in simulations with large LSP. Strong precipitation variability accompanied by a worse mean state implies that an optimal precipitation partitioning is critical to model tropical climate simulation.

Comparison of different approaches to fit log-normal mixtures on radar-derived precipitation data

Mixture models of precipitation frequency distributions describe the precipitation spectrum more adequately than simple probability distributions. The estimation of mixture distribution parameters by the maximum likelihood approach is commonly known. The reliability of these models and their parameters derived by the maximum likelihood method and by temporal classifications over Saxony was investigated using a radar-based precipitation product on a daily basis. Therefore, two different ‘unsupervised’ classification approaches were applied, a cluster analysis and a neural network, to derive subpopulation estimates. The temporal classification summarized high-resolution spatial grids of daily precipitation fields into 10 precipitation classes. The mixture model evaluation consists of two parts. First, an information criterion was calculated to describe the model complexity and the significance of the number of parameters. Second, the qualities of the fitted models were compared with the database through ordinary Kolmogorov–Smirnov tests and Kolmogorov–Smirnov tests in the context of field significance. Under the assumption of log-normal distributed precipitation, the neural network delivered the best mixture models for a spatial resolution of 2 and 3 km while the cluster analysis delivered the better initial parameters for an expectation-maximization algorithm on mixture distributions over the considered domain.

The Aqua-Planet Experiment (APE): Response to Changed Meridional SST Profile

This paper explores the sensitivity of Atmospheric General Circulation Model (AGCM) simulations to changes in the meridional distribution of sea surface temperature (SST). The simulations are for an aqua-planet, a water covered Earth with no land, orography or sea- ice and with specified zonally symmetric SST. Simulations from 14 AGCMs developed for Numerical Weather Prediction and climate applications are compared. Four experiments are performed to study the sensitivity to the meridional SST profile. These profiles range from one in which the SST gradient continues to the equator to one which is flat approaching the equator, all with the same maximum SST at the equator. The zonal mean circulation of all models shows strong sensitivity to latitudinal distribution of SST. The Hadley circulation weakens and shifts poleward as the SST profile flattens in the tropics. One question of interest is the formation of a double versus a single ITCZ. There is a large variation between models of the strength of the ITCZ and where in the SST experiment sequence they transition from a single to double ITCZ. The SST profiles are defined such that as the equatorial SST gradient flattens, the maximum gradient increases and moves poleward. This leads to a weakening of the mid-latitude jet accompanied by a poleward shift of the jet core. Also considered are tropical wave activity and tropical precipitation frequency distributions. The details of each vary greatly between models, both with a given SST and in the response to the change in SST. One additional experiment is included to examine the sensitivity to an off-equatorial SST maximum. The upward branch of the Hadley circulation follows the SST maximum off the equator. The models that form a single precipitation maximum when the maximum SST is on the equator shift the precipitation maximum off equator and keep it centered over the SST maximum. Those that form a double with minimum on the equatorial maximum SST shift the double structure off the equator, keeping the minimum over the maximum SST. In both situations only modest changes appear in the shifted profile of zonal average precipitation. When the upward branch of the Hadley circulation moves into the hemisphere with SST maximum, the zonal average zonal, meridional and vertical winds all indicate that the Hadley cell in the other hemisphere dominates.

Extreme flood response to short-duration convective rainfall in South-West Germany

Abstract. The 2 June 2008 flood-producing storm on the Starzel river basin in South-West Germany is examined as a prototype for organized convective systems that dominate the upper tail of the precipitation frequency distribution and are likely responsible for the flash flood peaks in Central Europe. The availability of high-resolution rainfall estimates from radar observations and a rain gauge network, together with indirect peak discharge estimates from a detailed post-event survey, provided the opportunity to study in detail the hydrometeorological and hydrological mechanisms associated with this extreme storm and the ensuing flood. Radar-derived rainfall, streamgauge data and indirect estimates of peak discharges are used along with a distributed hydrologic model to reconstruct hydrographs at multiple locations. Observations and model results are combined to examine two main questions, (i) assessment of the distribution of the runoff ratio for the 2008 flash flood and how it compares with other less severe floods; and (ii) analysis of how the spatial and temporal distribution of the extreme rainfall, and more specifically storm motion, controls the flood response. It is shown that small runoff ratios (less than 20%) characterized the runoff response and that these values are in the range of other, less extreme, flood events. The influence of storm structure, evolution and motion on the modeled flood hydrograph is examined by using the "spatial moments of catchment rainfall". It is shown that downbasin storm motion (in the range of 0.7–0.9 m s−1) had a noticeable impact on flood response by increasing the modeled flood peak by 13%.

Bias correction of daily precipitation simulated by a regional climate model: a comparison of methods

AbstractQuantifying the effects of future changes in the frequency of precipitation extremes is a key challenge in assessing the vulnerability of hydrological systems to climate change but is difficult as climate models do not always accurately simulate daily precipitation. This article compares the performance of four published techniques used to reduce the bias in a regional climate model precipitation output: (1) linear, (2) nonlinear, (3) γ‐based quantile mapping and (4) empirical quantile mapping. Overall performance and sensitivity to the choice of calibration period were tested by calculating the errors in the first four statistical moments of generated daily precipitation time series and using a cross‐validation technique. The study compared the 1961–2005 precipitation time series from the regional climate model HadRM3.0‐PPE‐UK (unperturbed version) with gridded daily precipitation time series derived from rain gauges for seven catchments spread throughout Great Britain. We found that while the first and second moments of the precipitation frequency distribution can be corrected robustly, correction of the third and fourth moments of the distribution is much more sensitive to the choice of bias correction procedure and to the selection of a particular calibration period. Overall, our results demonstrate that, if both precipitation data sets can be approximated by a γ‐distribution, the γ‐based quantile‐mapping technique offers the best combination of accuracy and robustness. In circumstances where precipitation data sets cannot adequately be approximated using a γ‐distribution, the nonlinear method is more effective at reducing the bias, but the linear method is least sensitive to the choice of calibration period. The empirical quantile mapping method can be highly accurate, but results were very sensitive to the choice of calibration time period. However, it should be borne in mind that bias correction introduces additional uncertainties, which are greater for higher order moments. Copyright © 2012 Royal Meteorological Society