H21G-0904
Verification of Multi-sensor Precipitation Reanalysis
The National Climatic Data Center (NCDC) and the National Weather Service (NWS) have implemented the NWS operational Multi-sensor Precipitation Estimation (MPE) algorithm with the historical NEXRAD data, the Digital Precipitation Array (DPA) products, in a reanalysis mode to develop a data set that is suited for long term climatological applications. The reanalysis is set up in a pilot domain over North and South Carolina for a 10 year period (1996-2007) and includes six WSR-88D sites. In this study we provide an evaluation of the multi-sensor precipitation reanalysis (MPR) over this region. In addition we provide comparisons with the operational Stage IV multi-sensor precipitation estimate. The evaluation of the MPR includes rain gauge (point) and radar-rainfall (pixel) comparisons at several temporal scales. A high density network from the Charlotte-Mecklenburg area (USGS) is used as it has a high temporal resolution (5-min) with a long period of record. Other rain gauge networks are from the North Carolina Mesonet and the U.S Climate Reference Network (USCRN). We present results of this evaluation via standard statistics, i.e. correlation coefficient, bias, and mean squared error. Another method of evaluation presented includes the mean squared error decomposition. In addition, we will investigate non-standard methods of evaluation such as Hovmoller diagrams, gridded correlation functions, and time series analysis.
H21G-0905
Understanding the climatologic features of uncertainty of high resolution precipitation products from satellites for advancing global applications over ungauged regions
High resolution precipitation products (HRPP) from satellites are becoming increasingly available for various global hydrologic applications. For advancing the application of these datasets, the associated uncertainty information will therefore be critical for users to understand the realistic limits to which these HRPPs can be applied over an ungauged region. However, this represents a paradox. Satellite rainfall uncertainty estimation requires ground validation (GV) precipitation data. On the other hand, satellite rainfall data will be most useful over ungauged regions in the developing world that are lacking in GV data. In this study, our aim is to focus on reconciling this paradox. The question we ask is: How much climatologic classification of the error regime is possible for characterizing uncertainty of satellite HRPPs? This is achieved by identifying the pertinent spatio-temporal characteristics of satellite rainfall uncertainty as a function of season and location using a six year (2002-2007) archive of NASA's TRMM Multi-satellite Precipitation Analysis (TMPA) data in the United States. Two TMPA products are used: 1) the infrared (IR) estimates at hourly and 0.25 degree spatial resolution known as 3B41RT, and 2) the combined passive microwave (PWM) and IR estimates at 3- hourly and 0.25 degree spatial resolution known as 3B42RT. NEXRAD Stage IV rainfall data is used as reference for ground validation. Various error metrics are computed for understanding the climatologic features of uncertainty associated with these TMPA products. Results using six years of TMPA data reveal insights on how we can leverage climatologic features of uncertainty for extrapolation over ungauged regions. We also attempt to understand the minimum set of error metrics that should be produced routinely by data producers to address most of the hydrologically-relevant features of uncertainty for a potential user of HRPP.
H21G-0906
Sub-Seasonal Forecast Skill Associated With Land Surface State Initialization: The Global Land Atmosphere Coupling Experiment-2
Given that soil moisture anomalies may persist for weeks to months, and given that soil moisture anomalies affect the surface energy budget and thus affect air temperature and the generation of precipitation, knowledge of sub-surface soil moisture conditions at the start of a seasonal or sub-seasonal forecast can potentially increase the skill of the forecast. GLACE-2, the 2nd phase of the Global Land-Atmosphere Coupling Experiment, is aimed at quantifying, across a broad range of state-of-art forecast models, the contribution of realistic soil moisture initialization to sub-seasonal forecast skill. Several universities and research institutes are currently engaged in GLACE-2, running a series of carefully designed forecast experiments with their Atmospheric General Circulation Models (AGCMs). Soil moisture initialization is based on an offline land surface model simulations with realistic (Global Soil Wetness Project 2) atmospheric forcing. In this presentation, we report on the current status of the GLACE-2 experiment and discuss the sub- seasonal forecast results found so far with the GEOS-5 (Goddard Earth Observing System Model, Version 5) AGCM in boreal summer.
H21G-0907
Evaluation of rainfall prediction for the Panama Canal watershed
The MM5 PSU/NCAR mesoscale meteorological model system, operating at HRC (Hydrologic Research Center), produces 54- hour forecasts at 18 km (Central America) and 6 km (Panama Canal watershed areas) domains. The forecasting system is executed twice a day (for 00Z and 12Z). The initial and boundary fields are from National Centers for Environmental Prediction (NCEP) global forecasting system (GFS) datasets. The 6-km forecasting precipitation in a gridded format is re-processed into mean area precipitation (MAP) by 11 sub-catchments which are defined by the Hydrology Section of the Panama Canal Authority (ACP). The MAP and other meteorological parameters are fed into hydrologic model which is operated routinely in ACP. The system design and the methodology for the evaluation will be presented. The goal is to evaluate the current model forecasting capability in the prediction of rainfall amounts which is important to control the Canal flows and facilitate ship passage through the Canal. The preliminary results show that the MM5 is able to capture the heavy rainfall events and provide adequate information into hydrologic forecast model.
H21G-0908
Statistical bias correction for daily precipitation in regional climate models over Europe
We design, apply and validate a methodology for correcting climate model output to produce internally consistent fields that have the same statistical intensity distribution as the observations. We refer to this as a statistical bias correction. Validation of the methodology is carried out using daily precipitation fields, defined over Europe, from the ENSEMBLES climate model dataset. The bias correction is calculated using data from 1961 to 1970, without distinguishing between seasons, and applied to seasonal data from 1991 to 2000. This choice of time periods is made to maximize the lag between calibration and validation within the ERA40 reanalysis period. Results show that the method performs unexpectedly well. Not only are the mean and other moments of the intensity distribution improved, as expected, but so are a drought and a heavy precipitation index, which depend on the autocorrelation spectra. Given that the corrections were derived without seasonal distinction and are based solely on intensity distributions, a statistical quantity oblivious of temporal correlations, it is encouraging to find that the improvements are present even when seasons and temporal statistics are considered. This encourages the application of this method to multi-decadal climate projections.
H21G-0909
Assessing Precipitation Flux in Low Layer Clouds Using Doppler Radars.
The low layer clouds because of their persistence and large cover have a substantial impact on Earth's energy budget and climate. Remote sensing retrievals of their parameters, such as liquid water and precipitation flux is important for climate model initialization and verification. Doppler radar observations of stratocumulus clouds were synthesized under the controlled framework of the Observing System Simulation Experiments (OSSEs). Cloud radar parameters, such as radar reflectivity, Doppler mean velocity and Doppler spectrum width, were obtained from the drop spectra generated using the latest version of the CIMMS explicit microphysics model (SAMEX) in simulations of cases observed during the ASTEX, DYCOMS-II and RICO field projects. The SAMEX model has been calibrated against reality in case studies which demonstrated that the model results compare favorably with observations. Based on performed OSSEs we quantitatively evaluate the contribution of various Doppler radar parameters to the improvement of retrievals of cloud liquid water and precipitation flux in low layer clouds. Performance of the formulation of cloud retrievals based on the radar reflectivity and mean Doppler velocity was contrasted with retrievals based on the radar reflectivity and Doppler spectrum width. The dependence of retrievals on precipitation intensity has been investigated.
H21G-0910
Impact of the Satellite-Gauge Based Precipitation Assimilation on Regional Downscaling
As recent studies have shown, the use of a regional model to downscale the large-scale analyses marginally improves simulated precipitation fields. This study investigates the impact of assimilating combined satellite- gauge products on a regional climate simulation over South America. The authors seek to recover the precipitation patterns during January of 2004, when precipitation analyses are available daily from a high- resolution, satellite-gauge based analysis over the continental South America. Here, precipitation assimilation is only effectuated in the same time scale as the rainfall analysis. As will be shown, rain rate assimilation not only increases the regional model precipitation simulation skill, but also improves the simulation of other variables influenced by the precipitation, as the South American circulation reveals precipitation patterns related to the low level flows. Due to the potential impact on land surface variables, improvements in monthly to seasonal predictions are expected.
H21G-0911
Precipitation Characteristics of APHRO_PR, High-Resolution Daily Precipitation Data.
We have been producing a long-term high-resolution daily precipitation data in Asia based on the individually
collected huge number of rain-gauge observations under the 'Asian Precipitation Highly-Resolved
Observational Data Integration Towards Evaluation of the Water Resources (APHRODITE)' project. This
data is intended to be used for water budget analysis, evaluation of global warming impacts on water
resources, validation of high-resolution climate model, trend analysis of precipitation extremes, and so on.
The interpolation algorithm is based on the combination of the spherical version of Shepard's distance-
weighting method (Shepard, 1968; Willmott et al., 1985) and Mountain Mapper method (Schaake et al.,
2004).
With spatially dense observations and careful quality control procedure, our dataset named APHRO_PR
derived reliable distribution pattern in annual mean precipitation and has no abnormal value. Moreover,
estimation bias for long-term average is improved by Mountain Mapper method in the high-elevations where
rain-gauge is insufficient. However, in the daily snapshots, the data remains some problems. The horizontal
distribution is too smooth and has 'bull's eye pattern' which is caused by Shepard's interpolation
method.
In this presentation, statistical characters of APHRO_PR are compared with other precipitation datasets.
Some of the challenges to improve interpolation technique are also discussed.
http://www.chikyu.ac.jp/precip/index.html
H21G-0912
Simulation of Orographically-Driven Precipitation in Southern California
The proximity of the Pacific Ocean to the Transverse and Peninsular Mountain Ranges of coastal Southern California may lead to significant, orographically-enhanced precipitation in the region. With abundant moisture, such as evidenced in Pineapple Express events or atmospheric rivers, this precipitation may lead to other hydrologic hazards as flash flooding, landslides or debris flows. Available precipitation observation networks are relatively sparse in the mountainous regions and often do not capture the spatial variation of these events with high resolution. This study aims to simulate the topographically-driven precipitation over Southern California with high spatial resolution using a simplified orographic precipitation model. The model employs potential theory flow to estimate steady state three-dimensional wind fields for given free stream velocity forcing winds, atmospheric moisture advection, and cloud and precipitation microphysics proposed by Kessler (1969). The advantage of this modeling set-up is the computational efficiency as compared to regional mesoscale models such as the MM5. For this application, the Southern California region, comprised of the counties of Santa Barbara, Ventura, Los Angeles, Orange, and San Diego, and portions of San Bernardino and Riverside counties, are modeled at a 3-km resolution. The orographic precipitation model is forced by free stream wind velocities given by the 700mb winds from the NCEP Reanalysis I dataset. Atmospheric moisture initial conditions are defined also by the NCEP Reanalysis I dataset, and updated 4x- daily with the available 6-hourly NCEP Reanalysis forcing. This paper presents a comparison of the simulated precipitation to observations for over a variety of spatial scales and over the historical wet season periods from October 2000 to April 2005. The comparison is made over several performance measurements including (a) the occurrence/non-occurrence of precipitation, (b) overall bias and correlation, (c) bias and correlation for precipitation exceeding given thresholds, and (d) the frequency distributions of non-zero precipitation. The results of simulation performance are compared to reported results of other orographically-driven precipitation and regional mesoscale model studies within the Western U.S.
H21G-0913
Diurnal Cycle Of The Simulated Precipitation By Regional Models
Recently, the diurnal cycle of precipitation in atmospheric models has been focused by the virtues of advanced precipitation estimate algorithms such as the Tropical Rainfall Measurement Mission (TRMM) Multi- satellite Precipitation Analysis (TMPA). Studies on the diurnal cycle in model experiments can serve the validation of various physical parameterizations. Further, the investigation of the model results can enhance our understanding of the important mechanisms embedded within atmospheric phenomena. This study examined the performance of two regional models with a focus on the diurnal variation of precipitation. It was found that the NCEP Regional Spectral Model (RSM) and Weather Forecasting and Research model (WRF) well reproduce the observed diurnal cycle of precipitation over East Asia, but the WRF is poor to describe semi-diurnal cycle compared to that of the RSM. A series of sensitivity experiments with different cumulus parameterization (CP), boundary layer (BL), microphysics (MP), and land surface (LS) schemes were also designed to examine the effect of physics schemes on the diurnal cycle of the simulated precipitation. The afternoon peak of precipitation over land was largely controlled by BL and LS schemes. The CP scheme was the most sensitive factor in relation to the diurnal cycle of precipitation over land, whereas over the oceans it was dominated by the MP processes.
H21G-0914
The Influence of SST Forcing on Simulated Precipitation and Drought
Previous studies suggest that ocean-atmosphere forcing by slowly varying sea surface temperature (SST) anomalies is a primary driver of seasonal-to-interannual hydroclimatic variability, including drought. In this study, we evaluate the influence of ocean-atmosphere forcing on precipitation and drought characteristics by comparing two ensembles of AGCM simulations forced with observed (interannually varying) monthly SST and their climatological annual cycle, respectively. Our results suggest that the influence of SST on the interannual variance of seasonal precipitation is significant throughout the tropics and many mid- and high latitude regions. However, SST anomalies do not significantly influence the autocorrelation of seasonal precipitation anomalies or the frequency, duration, and magnitude of drought events outside of the tropics-- i.e., while SST anomalies influence the likelihood of drought during a given season, ocean-atmosphere forcing does not significantly influence aggregate drought characteristics. These results bear important implications for seasonal-to-interannual precipitation prediction in general, and long-range drought prediction in particular.
H21G-0915
Great Plains Drought in Simulations of Twentieth Century
The Great Plains region of the United States was influenced by a number of multi-year droughts during the twentieth century. Most notable were the "Dust Bowl" drought of the 1930s and the 1950s Great Plains drought. In this study we evaluate the ability of three of the Coupled Global Climate Models (CGCMs) used in the Fourth Assessment Report (AR4) of the IPCC to simulate Great Plains drought with the same frequency and intensity as was observed during the twentieth century. The models chosen for this study are: GFDL CM 2.0, NCAR CCSM3, and UKMO HadCM3. We find that the models accurately capture the climatology of the hydrologic cycle of the Great Plains, but that they tend to overestimate the variability in Great Plains precipitation. We also find that in each model simulation at least one long-term drought occurs over the Great Plains region during their representations 20th Century Climate. The multi-year droughts produced by the models exhibit similar magnitudes and spatial scales as was observed during the twentieth century. This study also investigates the relative roles that external forcing from the tropical Pacific and local feedbacks between the land surface and the atmosphere have in the initiation and perpetuation of Great Plains drought in each model. We find that cool, La Nina-like conditions in the tropical pacific are often associated with long-term drought conditions over the Great Plains in GFDL CM 2.0 and UKMO HadCM3, but there appears to be no systematic relationship between tropical Pacific SST variability and Great Plains drought in CCSM3. It is possible the strong coupling between the land surface and the atmosphere in the NCAR model causes precipitation anomalies to lock into phase over the Great Plains thereby perpetuating drought conditions. Results from this study are intended to help assess whether or not these climate models are credible for use in the assessment of future drought over the Great Plains region of the United States.
H21G-0916
Predicting Seasonal Precipitation Over Sri Lanka Using Statistical Downscaling
October to November (ON) rains provide critical moisture for the growing period of the main rice cultivation season in Sri Lanka that lasts from October to March. Decisions on rice cultivation are made at a seasonal conference convened each year in September. Such decisions are presently based on climatological rainfall in the past 30 years, water levels in irrigation reservoirs and farmers' indigenous knowledge related to historical analogues of wind-direction in September. Past studies documented the skill in seasonal climate predictability in tropical regions in the boreal fall. In recent years there has been a proliferation of seasonal climate forecasts from Global Circulation Models (GCMs). Given the above facts, and the long record of precipitation observations at hundreds of rain gauges scattered across Sri Lanka, it is useful to examine whether statistical downscaling of precipitation could provide additional climate information that could be used for decision-making in agriculture and water resources management This paper analyzes the skill in ON precipitation totals over Sri Lanka by downscaling regional atmospheric variables, identified as affecting ON precipitation, from GCMs. A diagnostic analysis using historical precipitation observations at 145 rain gauges from 1961-2005 and reanalysis climate data reveals that ON precipitation is significantly correlated with September mean sea level pressure (MSLP) over the domain 40°E-270°E and 30°S-20°N and contemporaneous geopotential height anomalies at 200hPa and 850hPa over the domain 40°-270°E and 30°S-45°N. The Model Output Statistics (MOS) approach is utilized to develop seasonal predictions from hindcasts of September MSLP and October-November geopotential height anomalies at 200hPa and 850hPa from the ECHAM4.5 GCM (two versions: forced with constructed analogue SSTs; and persisted anomalies) and the fully-coupled NCEP-CFS GCM. ON precipitation forecasts are derived using cross validated Canonical Correlation Analysis (CCA). Precipitation skill assessments are made by computing Hit Skill Scores based on downscaled tercile – i.e. whether above-normal, near-normal or below-normal – precipitation.
H21G-0917
The South Asian monsoon in the 21st century: results from a perturbed-physics ensemble
We study the forecast precipitation over South Asia from an ensemble of general circulation model results from the climateprediction.net (CPDN) project, which has made available to the research community results from thousands of versions of the model Hadley climate model produced by varying sub-grid scale parameters. We use regression trees to identify the parameters that strongly influence regional temperature and precipitation. Next, we characterize ensemble members by how well they reproduce regional precipitation records to calculate the likelihood of different model versions. Previous energy balance modeling work by others shows the possibility of collapse of the monsoon if the albedo over the region rises above a threshold value, due to scattering aerosols. We study the CPDN ensemble members for evidence of threshold behavior in the monsoon. Some models versions show a decline in precipitation over the next few decades that lasts during the period of relatively high aerosol concentrations. We study whether the model versions that show persistent weakening in monsoon precipitation also exhibit changes in the frequency spectrum of rainfall. This is motivated by studies that have suggested how the change in spectral properties of the overturning of the thermohaline circulation might be related with the distance of the bifurcation point where it collapses, and thus used to detect an incipient bifurcation. The approach applies to any system in which threshold behavior involves weakening of an advection feedback and is described by a saddle-node bifurcation. The threshold- behavior model of the monsoon mentioned above shares this property. We summarize the ensemble's forecasts of monsoon in the region, and the prospects of anticipating changes in aerosol-driven precipitation reduction by studying changes in the frequency spectrum of rainfall.