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Mittelfristige Klimaprognose (MiKlip), an 8-yr German national research project on decadal climate prediction, is organized around a global prediction system comprising the Max Planck Institute Earth System Model (MPI-ESM) together [...]

Byline: Eric P. Salathe (1), L. Ruby Leung (2), Yun Qian (2), Yongxin Zhang (3) Abstract: Global climate models do not have sufficient spatial resolution to represent the atmospheric and land surface processes that determine the unique regional climate of the State of Washington. Regional climate models explicitly simulate the interactions between the large-scale weather patterns simulated by a global model and the local terrain. We have performed two 100-year regional climate simulations using the Weather Research and Forecasting (WRF) model developed at the National Center for Atmospheric Research (NCAR). One simulation is forced by the NCAR Community Climate System Model version 3 (CCSM3) and the second is forced by a simulation of the Max Plank Institute, Hamburg, global model (ECHAM5). The mesoscale simulations produce regional changes in snow cover, cloudiness, and circulation patterns associated with interactions between the large-scale climate change and the regional topography and land-water contrasts. These changes substantially alter the temperature and precipitation trends over the region relative to the global model result or statistical downscaling. To illustrate this effect, we analyze the changes from the current climate (1970--1999) to the mid twenty-first century (2030--2059). Changes in seasonal-mean temperature, precipitation, and snowpack are presented. Several climatological indices of extreme daily weather are also presented: precipitation intensity, fraction of precipitation occurring in extreme daily events, heat wave frequency, growing season length, and frequency of warm nights. Despite somewhat different changes in seasonal precipitation and temperature from the two regional simulations, consistent results for changes in snowpack and extreme precipitation are found in both simulations. Author Affiliation: (1) JISAO/CSES Climate Impacts Group, University of Washington, Seattle, WA, USA (2) Atmospheric Science and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA (3) Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO, USA Article History: Registration Date: 24/03/2010 Received Date: 04/06/2009 Accepted Date: 23/03/2010 Online Date: 05/05/2010

The problem of providing dispersion models with meteorological information from general atmospheric models used, for example, for weather forecasting is considered. As part of a generalized meteorological-to-dispersion model interface, a noniterative scheme diagnosing the surface layer characteristics from wind, temperature, and humidity profiles was developed. The scheme verification included long-term comparison with data of meteorological masts at Cabauw, the Netherlands, and Hyytiala, Finland. The algorithm compatibility and consistency with the High-Resolution Limited-Area Model (HIRLAM) was also checked, as this model is routinely used as a meteorological driver for the Air Quality and Emergency Modeling System (SILAM). The comparison with Cabauw mast data showed a good quantitative agreement between observed and diagnosed heat and momentum fluxes: the temporal correlation coefficient was ~0.8, bias was less than 10% of the absolute flux levels, regression slope deviated from unity for less than 20% with the intercept being less than 10% of the absolute flux values, and so on. In the case of complex surface features (Hyytiala mast in forest) the scheme proved to be robust with large deviations appearing only if the input profile data were taken outside the constant-flux layer. Comparison with the HIRLAM model showed qualitatively good agreement but also highlighted several differences between the goals, standards, and methodologies of meteorological and dispersion models. The scheme was implemented in SILAM, which served as the development platform. DOI: 10.1175/2009JAMC2210.1

This note examines the sensitivity of simulated U.S. warm-season precipitation in the Weather Research and Forecasting model (WRF), used as a nested regional climate model, to variations in model setup. Numerous options have been tested and a few of the more interesting and unexpected sensitivities are documented here. Specifically, the impacts of changes in convective and land surface parameterizations, nest feedbacks, sea surface temperature, and WRF version on mean precipitation are evaluated in 4-month-long simulations. Running the model over an entire season has brought to light some issues that are not otherwise apparent in shorter, weather forecast type simulations, emphasizing the need for careful scrutiny of output from any model simulation. After substantial testing, a reasonable model setup was found that produced a definite improvement in the climatological characteristics of precipitation over that from the National Centers for Environmental Prediction-National Center for Atmospheric Research global reanalysis, the dataset used for WRF initial and boundary conditions in this analysis. DOI: 10.1175/2009JAMC2186.1

Current land surface schemes used for mesoscale weather forecast models use the Jarvis-type stomatal resistance formulations for representing the vegetation transpiration processes. The Jarvis scheme, however, despite its robustness, needs significant tuning of the hypothetical minimum-stomatal resistance term to simulate surface energy balances. In this study, the authors show that the Jarvis-type stomatal resistance/transpiration model can be efficiently replaced in a coupled land-atmosphere model with a photosynthesis-based scheme and still achieve dynamically consistent results. To demonstrate this transformative potential, the authors developed and coupled a photosynthesis, gas exchange-based surface evapotranspiration model (GEM) as a land surface scheme for mesoscale weather forecasting model applications. The GEM was dynamically coupled with a prognostic soil moisture-soil temperature model and an atmospheric boundary layer (ABL) model. This coupled system was then validated over different natural surfaces including temperate C4 vegetation (prairie grass and corn field) and C3 vegetation (soybean, fallow, and hardwood forest) under contrasting surface conditions (such as different soil moisture and leaf area index). Results indicated that the coupled model was able to realistically simulate the surface fluxes and the boundary layer characteristics over different landscapes. The surface energy fluxes, particularly for latent heat, are typically within 10%-20% of the observations without any tuning of the biophysical-vegetation characteristics, and the response to the changes in the surface characteristics is consistent with observations and theory. This result shows that photosynthesis-based transpiration/stomatal resistance models such as GEM, despite various complexities, can be applied for mesoscale weather forecasting applications. Future efforts for understanding the different scaling parameterizations and for correcting errors for low soil moisture and/or wilting vegetation conditions are necessary to improve model performance. Results from this study suggest that the GEM approach using the photosynthesis-based soil vegetation atmosphere transfer (SVAT) scheme is thus superior to the Jarvis-based approaches. Currently GEM is being implemented within the Noah land surface model for the community Weather Research and Forecasting (WRF) Advanced Research Version Modeling System (ARW) and the NCAR high-resolution land data assimilation system (HRLDAS), and validation is under way.

The sensitivity of the Weather and Research Forecasting (WRF) model-simulated low-level winds in the Central Valley (CV) of California to uncertainties in the atmospheric forcing and soil initialization is investigated using scatter diagrams for a 5-day period in which meteorological conditions are typical of those associated with poor-air-quality events during the summer in the CV. It is assumed that these uncertainties can be approximated by two independent operational analyses. First, the sensitivity is illustrated using scatter diagrams and is measured in terms of the linear regression of the output from two simulations that differ in either the atmospheric forcing or the soil initialization. The spatial variation of the sensitivity is then investigated and is linked to the dominant low-level flows within the CV. The results from this case study suggest that the WRF-simulated low-level winds in the northern CV [i.e., the Sacramento Valley (SV)] are more sensitive to the uncertainties in the atmospheric forcing than to those in the soil initialization in the typical weather conditions during the summer that are prone to poor air quality in the CV. The simulated low-level winds in the southernmost part of the San Joaquin Valley (SJV) are more sensitive to the uncertainties in the soil initialization than they are in the SV. In the northern SJV, the simulated low-level winds are overall more sensitive to the uncertainties in the large-scale upper-level atmospheric forcing than to those in the soil initialization. This spatial variation in sensitivity reflects the important roles that the large-scale forcing, specified by the lateral boundary conditions and the local forcing associated with the soil state, play in controlling the low-level winds in the CV.

This paper evaluates the performance of the global precipitation rate retrieval algorithm for the Advanced Microwave Sounding Unit (AMSU) that was described in Part I of this paper. AMSU is in polar orbit on several National Ocean and Atmospheric Administration (NOAA) operational weather satellites. Predicted rms retrieval errors based on a 15-km resolution 0.5-1.0-mm/h MM5 truth were 0.88, 0.83, 1.13, and 3.04 for stratiform, warm rain, ice-free rain, and convective rain, respectively, which were averaged over all view angles ['or land and sea up to 73[degrees] latitude. For MM5 rates of 4-8 mm/h, these rms errors increased to 2.8, 3.4, 3.9, and 4.9 nun/h, respectively. The corresponding rms retrieval accuracies for MM5 hydrometeor water paths between 0.125 and 0.25 mm for rainwater, snow, and graupel were 0.19, 0.10, and 0.22 mm, respectively. The rms retrieval accuracy for the 0.125-0.25-m/s peak vertical wind was 0.08 m/s. Biases are small for cumulative precipitation estimates, although an upward correction factor of 1.37 is derived for convective precipitation rate probability distributions. Differences between these retrievals and those from the conically scanned Advanced Microwave Scanning Radiometer for the Earth Observing System instrument and an alternate NOAA AMSU algorithm are also characterized. Index Terms--Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), Advanced Microwave Sounding Unit (AMSU), microwave precipitation estimation, nficrowave radiative transfer, precipitation.

The May-June 2002 International [H.sub.2]O Project was held in the U.S. Southern Great Plains to determine ways that moisture data could be collected and utilized in numerical forecast models most effectively. We describe the surface and boundary layer components, and indicate how the data can be acquired. These data document the eddy transport of heat and water vapor from the surface to the atmosphere (in terms of sensible heat flux H and latent heat flux LE), as well as radiative, atmospheric, soil, and vegetative factors that affect it, so that the moisture and heat supply to the atmosphere can be related to surface properties both for observational studies and tests of land surface models. The surface dataset was collected at 10 surface flux towers at locations representing the major types of land cover and extending from southeast Kansas to the Oklahoma Panhandle. At each location, the components of the surface energy budget (H, LE, net radiation, and soil heat flux) are documented each half-hour, along with the weather (wind, temperature, mixing ratio, air pressure, and precipitation), soil temperature, moisture, and matric potential down to 70-90 cm beneath the surface at 9 of the 10 sites. Observations of soil and vegetation properties and their horizontal changes were taken near all 10 towers during periodic visits. Aircraft measurements of H and LE from repeated low-level flight tracks along three tracks collocated with the surface sites extend the flux tower measurements horizontally. We illustrate the effects of vegetation and soil moisture on the H and LE and their horizontal variability.

An increasing number of satellite-based rainfall products are now available in near-real time over the Internet to help meet the needs of weather forecasters and climate scientists, as well as a wide range of decision makers, including hydrologists, agriculturalists, emergency managers, and industrialists. Many of these satellite products are so newly developed that a comprehensive evaluation has not yet been undertaken. This article provides potential users of short-interval satellite rainfall estimates with information on the accuracy of such estimates. Since late 2002 the authors have been performing daily validation and intercomparisons of several operational satellite rainfall retrieval algorithms over Australia, the United States, and northwestern Europe. Short-range quantitative precipitation forecasts from four numerical weather prediction (NWP) models are also included for comparison. Synthesis of four years of daily rainfall validation results shows that the satellite-derived estimates of precipitation occurrence, amount, and intensity are most accurate during the warm season and at lower latitudes, where the rainfall is primarily convective in nature. In contrast, the NWP models perform better than the satellite estimates during the cool season when nonconvective precipitation is dominant. An optimal rain-monitoring strategy for remote regions might therefore judiciously combine information from both satellite and NWP models.

An intercomparison of four mesoscale numerical prediction models that could lead to the selection of a model for use in the theater of operations by United States Air Force (USAF) meteorological personnel is described. Mesoscale numerical prediction models have matured, and recent advances in computer hardware make this a realizable objective. Two studies were launched to determine if a mesoscale model could be used operationally in theater and to select the model that produced the best forecast under simulated operational conditions. Of prime concern was not whether the model could produce reliable forecasts in data-rich areas, but how well the models operated and thus produced forecasts in data-sparse areas. The first study did an overall review of the available mesoscale numerical weather prediction models resulting in a general ranking of the models by expected forecast ability and operational maturity. At the conclusion of this study it became apparent that a more in-depth analysis was needed to distinguish among the higher-ranking models. Thus, this study was initiated. This study compared four models for quality of forecasts in different climate regions in the world. Two are considered state-of-the-art models that could easily be made operational. These are the Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model 5 (MM5) and the Colorado State University Regional Atmospheric Modeling System (RAMS). The third model was the Navy Operational Regional Prediction System Version 6 (NORAPS6), the navy's operational regional forecast model. The fourth model is the current USAF mesoscale model, the Relocatable Window Model (RWM), that was used to provide a baseline of the current USAF capability. The models were scored by comparing the forecast values with observations. The relative ranking of the models varied with parameter, but overall, the rank order was RAMS, MM5, NORAPS6, and RWM. The score disparity between the models was not large.

A prototype advanced model output statistics (MOS) forecast system that was entered in the 1996-97 National Collegiate Weather Forecast Contest is described and its performance compared to that of widely available objective guidance and to contest participants. The prototype system uses an optimal blend of aviation (AVN) and nested grid model (NGM) MOS forecasts, explicit output from the NGM and Eta guidance, and the latest surface weather observations from the forecast site. The forecasts are totally objective and can be generated quickly on a personal computer. Other 'objective' forms of guidance tracked in the contest are 1) the consensus forecast (i.e., the average of the forecasts from all of the human participants), 2) the combination of NGM raw output (for precipitation forecasts) and NGM MOS guidance (for temperature forecasts), and 3) the combination of Eta Model raw output (for precipitation forecasts) and AVN MOS guidance (for temperature forecasts). Results show that the advanced MOS system finished in 20th place out of 737 original entrants, or better than approximately 97% of the human forecasters who entered the contest. Moreover, the advanced MOS system was slightly better than consensus (23d place). The fact that an objective forecast system finished ahead of consensus is a significant accomplishment since consensus is traditionally a very formidable 'opponent' in forecast competitions. Equally significant is that the advanced MOS system was superior to the traditional guidance products available from the National Centers for Environmental Prediction (NCEP). Specifically, the combination of NGM raw output and NGM MOS guidance finished in 175th place, and the combination of Eta Model raw output and AVN MOS guidance finished in 266th place. The latter result is most intriguing since the proposed elimination of all NGM products would likely result in a serious degradation of objective products disseminated by NCEP, unless they are replaced with equal or better substitutes. On the other hand, the positive performance of the prototype advanced MOS system shows that it is possible to create a single objective product that is not only superior to currently available objective guidance products, but is also on par with some of the better human forecasters.

Limited-area models (LAMs) are presently used for a wide variety of research and operational forecasting applications, and such use will likely expand greatly as the rapid increase in the performance/price ratio of computers and workstations makes LAMs more accessible to novice users. The robustness of these well-tested and documented models will make it tempting for many to consider them as turn-key systems that can be used without any experience or formal training in numerical weather prediction. This paper is intended as a tutorial and caution for such prospective model users, with the specific purpose of illustrating that, in spite of advanced physical-process parameterizations and high resolutions permitted by faster computers, and modem mesoscale data for initial conditions, there is still a basic limitation to predictability with a LAM - lateral boundary conditions (LBC). Illustrations are provided of previous work that show the serious negative effects of LBCs, and guidelines are provided for helping to minimize their negative impact on forecast quality.

Experience in performing real-time mesoscale numerical prediction forecasts using the Regional Atmospheric Modeling System (RAMS) over Colorado for a winter season on high-performance workstations is summarized. Performance evaluation is done for specific case studies and, statistically, for the entire winter season. RAMS forecasts are also compared with nested grid model forecasts. In addition, RAMS precipitation forecasts with a simple 'dump bucket' scheme are compared with explicit, bulk microphysics parameterization schemes. The potential applications and political/social problems of having a readily accessible, real-time mesoscale forecasting capability on low-cost, high-performance workstations is discussed.

The possibility of using forecast errors originating from the finite-time dominant linear modes for the prediction of forecast skill for a primitive equation regional forecast model is studied. This is similar to the method for skill prediction suggested by several other authors using simplified models. Two main problems associated with a sophisticated forecast model not considered in these other studies are investigated. 1) The number of degrees of freedom is typically too large for the evaluation of the spectrum of dominant modes associated with the linear error evolution equation, and 2) many different, physically meaningful, error measures may be used for this model and the dominant linear modes may be sensitive to the selection of the error measure. It is shown first that the finite-time dominant linear solutions can be computed with sufficient accuracy for the complex forecast model using standard 'power' method with a small number of iterations for all error measures considered in this study. The forecast skill is then estimated using the nonlinear forecast errors originating from the initial errors that are defined by these measure-dependent dominant solutions. These results show that the estimated forecast skill is very sensitive to the choice of error measure used for the computation of the finite-time dominant modes.

To elucidate his numerical technique and to examine the effectiveness of geostrophic initial winds, Lewis Fry Richardson carried out an idealized forecast using the linear shallow-water equations and simple analytical pressure and velocity fields. This barotropic forecast has been repeated and extended using a global numerical model, and the results are presented in this paper. Richardson's conclusions regarding the use of geostrophic winds as initial data are reconsidered. An analysis of Richardson's data into normal modes shows that almost 85% of the energy is accounted for by a single eigenmode, the gravest symmetric rotational Hough mode, which travels westward with a period of about five days. This five-day wave has been detected in analyses of stratospheric data. It is striking that the fields chosen by Richardson on considerations of smoothness should so closely resemble a natural oscillation of the atmosphere. The numerical model employed in this study uses an implicit differencing technique, which is stable for large time steps. The numerical instability that would have destroyed Richardson's barotropic forecast, had it been extended, is thereby circumvented. It is sometimes said that computational instability was the cause of the failure of Richardson's baroclinic forecast, for which he obtained a pressure tendency value two orders of magnitude too large. However, the initial tendency is independent of the time step (at least for the explicit scheme used by Richardson). In fact, the spurious tendency resulted from the presence of unrealistically large high frequency gravity-wave components in the initial fields. High-frequency oscillations are also found in the evolution starting from the idealized data in the barotropic forecast. They are shown to be due to the gravity-wave components of the initial data. These oscillations may be removed by a slight modification of the initial fields. This initialization is effected by means of a simple digital filtering technique, which is applicable not only to the linear equations used here but also to a general nonlinear system.

Using heuristic arguments, Businger presented a derivation of the so-called Dyer-Businger formulas for nondimensional wind and temperature profiles. In this note it is shown that by slightly changing this 'derivation,' different expressions are obtained that almost coincide with the original ones for 0 < -z/L < -1, but that behave differently in the free-convection region. The new formula for temperature appears to have a correct free-convection behavior: that is, it becomes proportional to [(-z/L).sub.-1/3]. This note is primarily 'historical' in nature.

A scheme is proposed that relates surface flux densities of sensible heat, latent heat, and momentum to routine weather data. The scheme contains parameterizations concerning the radiation components and the surface energy flux densities. The parameterizations are developed and examined using observations from 1987 of a grass-covered surface at Cabauw in the Netherlands. It is shown that improvements in the parameterizations are achieved by incorporating an albedo dependence on solar elevation, a longwave downward radiation with a correction for the amount of high clouds, and a soil heat flux with a soil temperature approximated by a 24-h-mean 2-m temperature. In addition, the Penman-Monteith concept for the latent heat flux is utilized with a simple one-parameter surface resistance, which depends on atmospheric moisture deficit in particular. Special attention is paid to the treatment of surface inhomogeneities. A distinction is made between stable conditions, when measurements in the lower 10 m appear to be in equilibrium with the local surface, and unstable conditions, when measurements seem to be influenced by deviating upstream surface conditions. A constant roughness length for heat above grassland of 1 mm is applied. Finally, the scheme as a whole is evaluated and compared with a previous approach by A. P. van Ulden and A. A.M. Holtslag. It appears that in particular the sensible heat flux is improved with the new scheme. This can be ascribed mostly to the replacement of the modified Priestley-Taylor by the Penman-Monteith formulation and by a better representation of the surface temperature.