H24C-01 INVITED
New Pathways to Better Precipitation Prediction in the GPM Era
The Global Precipitation Measurement (GPM) Mission is an international satellite mission to unify and advance global precipitation measurements from a constellation of dedicated and operational microwave sensors. The GPM Core Observatory, which will serve as the calibration standard for the constellation sensors, is scheduled for launch in June 2013. The mission is to provide the next-generation global precipitation products for scientific research and societal applications including quantitative precipitation estimation (QPE) and quantitative precipitation forecast (QPF). Space-based precipitation measurements, when used with contemporaneous analyses of the atmospheric state, can provide a wealth of information about moist physical processes, whose representation is a major source of uncertainty in numerical models used for precipitation forecasting. GPM will deliver global precipitation estimates of unprecedented measurement accuracy, temporal sampling, and spatial coverage, offering new opportunities to pursue innovative usage of these data to diagnose and correct deficiencies in model physics. This presentation will give an overview of the GPM Mission and its science plans, with a focused discussion on how space-based precipitation information can be used to improve model physics within the general framework of data assimilation and statistical parameter estimation to advance precipitation analysis and forecast skills.
H24C-02
Combined Radar-Radiometer Retrieval of Precipitation Structures
Satellite rainfall estimates from microwave radiometers (e.g., SSM/I, AMSR-E), and, more recently, spaceborne radar (TRMM, CloudSat) have provided the ability to measure precipitation globally. However, the remote sensing of rainfall requires assumptions in both radar and passive microwave radiometer algorithms that may not be uniformly applicable in all regions. These assumptions are required due to the limitations of the instrument and related to the wide variety of rainfall physical processes (e.g., convective vs. stratiform, warm vs. cold cloud). These processes lead to differences in the raindrop size distribution (DSD) and cloud profiles on a wide range of spatial and temporal scales. Radar algorithms are particularly sensitive to the DSD, whereas radiometers, while less sensitive to DSD, can only sense column-integrated liquid water over a large area, where rainfall is unlikely to be uniform. Combined retrievals incorporate the high-resolution radar profiles with the areally integrated constraint provided by the radiometer to produce a rainfall estimate that is consistent with both. However, the mismatch of resolution and field of view between radar and radiometer measurements complicates the formulation of combined algorithms. An optimal estimation methodology has been applied to adjust radar retrievals using radiometer-observed brightness temperatures, incorporating a priori knowledge about the spatial structure of raining systems in the retrieval. The adjustments made by this algorithm to radar-only retrievals are consistent with the known biases of the radar algorithm caused by DSD assumptions. The combined retrieval, therefore, has the potential to improve observations of the distribution of global rainfall and rainfall processes.
H24C-03
Re-formulation and Validation of Cloud Microphysics Schemes
The research focuses on improving quantitative precipitation forecasts by removing significant uncertainties in current cloud microphysics schemes embedded in models such as WRF and MM5 and cloud-resolving models such as GCE. Reformulation of several production terms in these microphysics schemes was found necessary. When estimating four graupel production terms involved in the accretion between rain, snow and graupel, current microphysics schemes assumes that all raindrops and snow particles are falling at their appropriate mass-weighted mean terminal velocities and thus analytic solutions are able to be found for these production terms. Initial analysis and tests showed that these approximate analytic solutions give significant and systematic overestimates of these terms, and, thus, become one of major error sources of the graupel overproduction and associated extreme radar reflectivity in simulations. These results are corroborated by several reports. For example, the analytic solution overestimates the graupel production by collisions between raindrops and snow by up to 230%. The structure of "pure" snow (not rimed) and "pure graupel" (completely rimed) in current microphysics schemes excludes intermediate forms between "pure" snow and "pure" graupel and thus becomes a significant reason of graupel overproduction in hydrometeor simulations. In addition, the generation of the same density graupel by both the freezing of supercooled water and the riming of snow may cause underestimation of graupel production by freezing. A parameterization scheme of the riming degree of snow is proposed and then a dynamic fallspeed-diameter relationship and density- diameter relationship of rimed snow is assigned to graupel based on the diagnosed riming degree. To test if these new treatments can improve quantitative precipitation forecast, the Hurricane Katrina and a severe winter snowfall event in the Sierra Nevada Range are selected as case studies. A series of control simulation and sensitivity tests was conducted for these two cases. Two statistical methods are used to compare simulated radar reflectivity by the model with that detected by ground-based and airborne radar at different height levels. It was found that the changes made in current microphysical schemes improve QPF and microphysics simulation significantly.
H24C-04
The transition to strong convection: temperature/moisture dependence and mesoscale clusters
Recent work has shown that observations of tropical precipitation conform to a number of properties associated with critical phenomena in other systems (Peters and Neelin, 2006, Nature Phys.). Here some of these properties are used to probe the physics of tropical convection empirically in Tropical Rainfall Measuring Mission microwave and radar data. The power law pick-up of the ensemble-average precipitation as a function of column water vapor on tropospheric temperature is determined, and is found to differ from the simplest expectations from convective parameterizations. The empirically determined critical surface thus provides a new test against which convective parameterizations can be compared. The frequency of occurrence of strong precipitation has a characteristic sharp drop as the system approaches the critical point, with exponential decay above critical. A large fraction of the precipitation occurs near and above critical. Mesoscale convective cluster average size picks up sharply at critical, and the size distribution exhibits a scale-free range near critical. Sea surface temperature (SST) is shown not to have a strong effect on the critical pickup of precipitation. The effect of SST occurs instead via the fraction of time spent near critical over regions of warm SST. These new diagnostics are consistent with some assumptions of current convective parameterizations, but also suggest additional properties that should be included. http://www.atmos.ucla.edu/\tilde{}csi/REF
H24C-05 INVITED
US CLIVAR MJO Working Group: Efforts to Establish and Improve Subseasonal Predictions
In spring 2006, US CLIVAR established the Madden-Julian Oscillation (MJO) Working Group (MJOWG). The formation of this 2-year limited lifetime WG was motivated by: 1) the wide range of weather and climate phenomena that the MJO interacts with and influences, 2) the fact that the MJO represents an important, and as yet unexploited, source of predictability at the subseasonal time scale, 3) the considerable shortcomings in our global climate and forecast models in representing the MJO, and 4) the need for coordinating the multiple threads of programmatic and investigator level research on the MJO. Near-term tasks involve the development of diagnostics for assessing model performance in both climate simulation and extended-range/subseasonal forecast settings as well as develop a consistent and coordinated approach to subseasonal, specifically MJO, forecasting. At present this effort includes participation from NCEP, ECMWF, BMRC, UKMO, CMA and ESRL/NOAA. The purpose of this presentation is to make the community aware of these activities. This will include discussing the diagnostics that have been developed for assessing model performance in simulating/predicting the MJO, show results of their application to a number of present-day GCMs, and also discuss the MJO forecast metrics and the plans to develop a multi-model ensemble forecast for the MJO. For additional details, see www.usclivar.org. http://www.usclivar.org/Organization/MJO_WG.html
H24C-06
A WCRP GEWEX - CliC Initiative: High Altitude - Latitude Hydrology
Within Earth Sciences many of the disciplines relevant to hydrology and even hydrological sciences itself are disperse when dealing with cold climates/regions of the world. The links between cold region hydrology and temperate or tropical climate zones are weak or even lacking. Geographically, the links are missing between high latitude /cold region hydrology and high altitude/mountain hydrology. To understand global (climate) changes and its effect on water resources this gap needs to be bridged. Most of our fresh water resources originate in mountainous regions and/or cold regions. Changes in these environments lead to hydrological implications for water resources management which are currently poorly understood and not very predictable on any time and spatial scale. This gap is recognised within international science programmes such as the Global Energy and Water Cycle Experiment (GEWEX), and the Climate and Cryosphere Project (CliC), both part of the World Climate Research Programme (WCRP). Even extremely important initiatives such as the International Polar Year (IPY) or Northern Eurasian Earth Science Partnership Initiative (NEESPI) do not fully bridge this gap. A new initiative, High Altitude - Latitude Hydrology will bring together, different disciplines, various research communities and individual scientists to help bridge this gap. This should lead to more visibility of critical issues with funding agencies and is expected to result in an increase in expertise, knowledge and increased technological and experimental capability.
H24C-07
El Nińo/Southern Oscillation Impact on Rainfall Over South America: A Bayesian Approach to Improve its Forecasts.
The goal of this work is to improve the skill of long-range predictions of rainfall over South-Eastern South America (SESA). We follow a Bayesian approach, i.e. a combination of empirical relationships and numerical model's output. In the present work we focus on the period October-November, during which anomalous precipitation over SESA is significantly linked to El Niño Southern Oscillation (ENSO). The period of study is 1957-1998. The empirical model includes a linear regression of an index based on the observed anomalies in meridional wind over South America at 200 hPa and in mean precipitation over SESA during both El Niño and La Niña events; the correlation between these two timeseries is 0.7. The dynamical meaning of this empirical model lies in an observed relationship between mean anomalies over SA during ENSO events: enhanced precipitation over SESA is accompanied by an anomalous cyclone centered east of southern Brazil during El Niño events and vice versa during La Niña events. The wind index is defined as the mean of that quantity at the location of intense meridional wind over SESA during ENSO events. The numerical model output consists of a non-bias corrected ensemble of 4 runs by the UCLA AGCM in its 2 lon x 2.5 lat x 29 layers configuration. Ensemble members are 42-year long simulations with observed sea surface temperatures. The initial conditions correspond to December 1st, 1956 plus small random perturbations. We find that our method can significantly improve precipitation forecasts over SESA during ENSO events. The skill score of the bias corrected ensemble, in reference to climatology, is 0.1; the corresponding value for the Bayesian forecast is 0.22 when all the years in the timeserie are considered. If only ENSO events are considered, the skill score increases from -0.7 for the bias corrected ensemble mean to 0.27 for the Bayesian forecast. The results for other seasons will be presented at the Conference.
H24C-08
On the seasonal predictability of daily rainfall characteristics over Indonesia
Using Indonesia as a case study, we investigate the seasonal predictability of monsoonal rainfall in terms of its daily characteristics, including rainfall frequency, mean daily intensity, mean length of dry spells, as well as the onset date of the rainy season. Our methodology consists of retrospective seasonal forecasts made with the ECHAM4.5 atmospheric GCM driven by SST predictions made with the constructed analog method (1979-2002). The seasonal forecast is then "downscaled" to the station scale using a non-homogeneous hidden Markov model, to produce large ensembles of stochastic daily rainfall sequences from which the statistic of interest is calculated and compared against station data. Particular attention is paid to probabilistic measures of skill, including conditional exceedance probabilities. Our key findings are that rainfall frequency and mean dry-spell length are more skillfully hindcast than the seasonal total of rainfall for the September–December season. The date of monsoon onset is also found to be highly predictable in these experiments. Supporting evidence will be presented from analyses of the spatial coherence properties of these various daily rainfall characteristics computed from station data. We argue that these findings may have important potential uses in agricultural and water resource management.