With support from NASA's Modeling and Analysis Program, we have recently developed the NASA Unified-Weather Research and Forecasting model (NU-WRF). NU-WRF is an observation-driven integrated modeling system that represents aerosol, cloud, precipitation and land processes at satellite-resolved scales. "Satellite-resolved" scales (roughly 1-25 km), bridge the continuum between local (microscale), regional (mesoscale) and global (synoptic) processes. NU-WRF is a superset of the National Center for Atmospheric Research (NCAR) Advanced Research WRF (ARW) dynamical core model, achieved by fully integrating the GSFC Land Information System (LIS, already coupled to WRF), the WRF/Chem enabled version of the GOddard Chemistry Aerosols Radiation Transport (GOCART) model, the Goddard Satellite Data Simulation Unit (G-SDSU), and custom boundary/initial condition preprocessors into a single software release, with source code available by agreement with NASA/GSFC. Full coupling between aerosol, cloud, precipitation and land processes is critical for predicting local and regional water and energy cycles. Published by Elsevier Ltd.

The meteorological predictions of fully coupled air-quality models running in "feedback" versus "no-feedback" simulations were compared against each other and observations as part of Phase 2 of the Air Quality Model Evaluation International Initiative. In the "no-feedback" mode, the aerosol direct and indirect effects were disabled, with the models reverting to either climatologies of aerosol properties, or a no-aerosol weather simulation. In the "feedback" mode, the model-generated aerosols were allowed to modify the radiative transfer and/or cloud formation parameterizations of the respective models. Annual simulations with and without feedbacks were conducted on domains over North America for the years 2006 and 2010, and over Europe for the year 2010.The incorporation of feedbacks was found to result in systematic changes to forecast predictions of meteorological variables, both in time and space, with the largest impacts occurring in the summer and near large sources of pollution. Models incorporating only the aerosol direct effect predicted feedback-induced reductions in temperature, surface downward and upward shortwave radiation, precipitation and PBL height, and increased upward shortwave radiation, in both Europe and North America. The feedback response of models incorporating both the aerosol direct and indirect effects varied across models, suggesting the details of implementation of the indirect effect have a large impact on model results, and hence should be a focus for future research. The feedback response of models incorporating both direct and indirect effects was also consistently larger in magnitude to that of models incorporating the direct effect alone, implying that the indirect effect may be the dominant process. Comparisons across modelling platforms suggested that direct and indirect effect feedbacks may often act in competition: the sign of residual changes associated with feedbacks often changed between those models incorporating the direct effect alone versus those incorporating both feedback processes.Model comparisons to observations for no-feedback and feedback implementations of the same model showed that differences in performance between models were larger than the performance changes associated with implementing feedbacks within a given model. However, feedback implementation was shown to result in improved forecasts of meteorological parameters such as the 2 m surface temperature and precipitation. These findings suggest that meteorological forecasts may be improved through the use of fully coupled feedback models, or through incorporation of improved climatologies of aerosol properties, the latter designed to include spatial, temporal and aerosol size and/or speciation variations. Crown Copyright (C) 2014 Published by Elsevier Ltd. All rights reserved.

Fully-coupled air-quality models running in "feedback" and "no-feedback" configurations were compared against each other and observation network data as part of Phase 2 of the Air Quality Model Evaluation International Initiative. In the "no-feedback" mode, interactions between meteorology and chemistry through the aerosol direct and indirect effects were disabled, with the models reverting to climatologies of aerosol properties, or a no-aerosol weather simulation, while in the "feedback" mode, the model-generated aerosols were allowed to modify the models' radiative transfer and/or cloud formation processes. Annual simulations with and without feedbacks were conducted for domains in North America for the years 2006 and 2010, and for Europe for the year 2010. Comparisons against observations via annual statistics show model-to-model variation in performance is greater than the within-model variation associated with feedbacks. However, during the summer and during intense emission events such as the Russian forest fires of 2010, feedbacks have a significant impact on the chemical predictions of the models.The aerosol indirect effect was usually found to dominate feedbacks compared to the direct effect. The impacts of direct and indirect effects were often shown to be in competition, for predictions of ozone, particulate matter and other species. Feedbacks were shown to result in local and regional shifts of ozone-forming chemical regime, between NOx- and VOC-limited environments. Feedbacks were shown to have a substantial influence on biogenic hydrocarbon emissions and concentrations: North American simulations incorporating both feedbacks resulted in summer average isoprene concentration decreases of up to 10%, while European direct effect simulations during the Russian forest fire period resulted in grid average isoprene changes of -5 to +12.5%. The atmospheric transport and chemistry of large emitting sources such as plumes from forest fires and large cities were shown to-be strongly impacted by the presence or absence of feedback mechanisms in the model simulations. Summertime model performance for ozone and other gases was improved through the inclusion of indirect effect feedbacks, while performance for particulate matter was degraded, suggesting that current parameterizations for in- and below cloud processes, once the cloud locations become more directly influenced by aerosols, may over- or under-predict the strength of these processes. Process parameterization-level comparisons of fully coupled feedback models are therefore recommended for future work, as well as further studies using these models for the simulations of large scale urban/industrial and/or forest fire plumes. Crown Copyright (C) 2014 Published by Elsevier Ltd. All rights reserved.

The forthcoming Global Precipitation Measurement (GPM) Mission will provide next-generation precipitation observations from a constellation of satellites. Since precipitation by nature has large variability and low predictability at cloud-resolving scales, the impact of precipitation data on the skills of mesoscale numerical weather prediction (NWP) is largely affected by the characterization of background and observation errors and the representation of nonlinear cloud/precipitation physics in an NWP data assimilation system.We present a data impact study on the assimilation of precipitation-affected microwave (MW) radiances from a pre-GPM satellite constellation using the Goddard WRF Ensemble Data Assimilation System (Goddard WRF-EDAS). A series of assimilation experiments are carried out in a Weather Research Forecast (WRF) model domain of 9 km resolution in western Europe. Sensitivities to observation error specifications, background error covariance estimated from ensemble forecasts with different ensemble sizes, and MW channel selections are examined through single-observation assimilation experiments. An empirical bias correction for precipitation-affected MW radiances is developed based on the statistics of radiance innovations in rainy areas. The data impact is assessed by full data assimilation cycling experiments for a storm event that occurred in France in September 2010. Results show that the assimilation of MW precipitation observations from a satellite constellation mimicking GPM has a positive impact on the accumulated rain forecasts verified with surface radar rain estimates. The case-study on a convective storm also reveals that the accuracy of ensemble-based background error covariance is limited by sampling errors and model errors such as precipitation displacement and unresolved convective scale instability.

The Global Precipitation Measurement (GPM) mission is an international partnership co-led by NASA and the Japan Aerospace Exploration Agency (JAXA). The mission centers on the GPM Core Observatory and consists of an international network, or constellation, of additional satellites that together will provide next-generation global observations of precipitation from space. The GPM constellation will provide measurements of the intensity and variability of precipitation, three-dimensional structure of cloud and storm systems, the microphysics of ice and liquid particles within clouds, and the amount of water falling to Earth’s surface. Observations from the GPM constellation, combined with land surface data, will improve weather forecast models; climate models; integrated hydrologic models of watersheds; and forecasts of hurricanes/typhoons/cylcones, landslides, floods and droughts. The GPM Core Observatory carries an advanced radar/radiometer system and serves as a reference standard to unify precipitation measurements from all satellites that fly within the constellation. The GPM Core Observatory improves upon the capabilities of its predecessor, the NASA-JAXA Tropical Rainfall Measuring Mission (TRMM), with advanced science instruments and expanded coverage of Earth’s surface. The GPM Core Observatory carries two instruments, the NASA-supplied GPM Microwave Imager (GMI) and the JAXA-supplied Dual-frequency Precipitation Radar (DPR). The GMI measures the amount, size, intensity and type of precipitation, from heavy-tomoderate rain to light rain and snowfall. The DPR provides three-dimensional profiles and intensities of liquid and solid precipitation. The French Centre National d’Études Spatiales (CNES), the Indian Space Research Organisation (ISRO), the U.S. National Oceanic and Atmospheric Administration (NOAA), the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), and the U.S. Department of Defense are partners with NASA and JAXA. The GPM Core Observatory was launched from JAXA’s Tanegashima Space Center on an H-IIA launch vehicle on February 28, 2014 Japan Standard Time (JST). The mission has completed its checkout and commissioning phase and is in Operations Phase. The current status and early results will be discussed.

Two mixed-phase precipitation events were observed on 21 September and 20 October 2010 over the southern part of Finland during the Light Precipitation Validation Experiment (LPVEx). These events have been simulated using the Weather Research and Forecasting Model coupled with spectral bin microphysics (WRF-SBM). The detailed ice-melting scheme with prognosis of the liquid water fraction during melting enables explicit simulation of microphysical properties in the melting layer. First, the simulations have been compared with C-band 3D radar measurements for the purpose of evaluating the overall profiles of cloud and precipitation. The simulation has some artificial convective patterns and errors in the forecast displacement of the precipitation system. The overall overestimation of reflectivity is consistent with a bias toward the range characterized by large-diameter droplets in the surface drop size distribution. Second, the structure of the melting bands has been evaluated against vertically pointing K-band radar measurements. A peak in reflectivity and a gradual change in Doppler velocity are observed and similarly simulated in the common temperature range from approximately 0 degrees to 3 degrees C. The effectiveness of the time-dependent melting scheme has been justified by intercomparison with a corresponding simulation using an instantaneous melting scheme. A weakness of the new melting scheme is that melting particles having high liquid water fractions on the order of 80%-90% cannot be simulated. This situation may cause underestimation of radar reflectivity in the melting layer because of the assumptions of melting-particle structure used to calculate the scattering properties.

The increasing availability of precipitation observations from space, e.g., from the Tropical Rainfall Measuring Mission (TRMM) and the forthcoming Global Precipitation Measuring (GPM) Mission, has fueled renewed interest in developing frameworks for downscaling and multi-sensor data fusion that can handle large data sets in computationally efficient ways while optimally reproducing desired properties of the underlying rainfall fields. Of special interest is the reproduction of extreme precipitation intensities and gradients, as these are directly relevant to hazard prediction. In this paper, we present a new formalism for downscaling satellite precipitation observations, which explicitly allows for the preservation of some key geometrical and statistical properties of spatial precipitation. These include sharp intensity gradients (due to high-intensity regions embedded within lower-intensity areas), coherent spatial structures (due to regions of slowly varying rainfall), and thicker-than-Gaussian tails of precipitation gradients and intensities. Specifically, we pose the downscaling problem as a discrete inverse problem and solve it via a regularized variational approach (variational downscaling) where the regularization term is selected to impose the desired smoothness in the solution while allowing for some steep gradients (called ℓ1-norm or total variation regularization). We demonstrate the duality between this geometrically inspired solution and its Bayesian statistical interpretation, which is equivalent to assuming a Laplace prior distribution for the precipitation intensities in the derivative (wavelet) space. When the observation operator is not known, we discuss the effect of its misspecification and explore a previously proposed dictionary-based sparse inverse downscaling methodology to indirectly learn the observation operator from a data base of coincidental high- and low-resolution observations. The proposed method and ideas are illustrated in case studies featuring the downscaling of a hurricane precipitation field.

Convection is the primary transport process in the Earth's atmosphere. About two-thirds of the Earth's rainfall and severe floods derive from convection. In addition, two-thirds of the global rain falls in the tropics, while the associated latent heat release accounts for three-fourths of the total heat energy for the Earth's atmosphere. Cloud-resolving models (CRMs) have been used to improve our understanding of cloud and precipitation processes and phenomena from micro-scale to cloud-scale and mesoscale as well as their interactions with radiation and surface processes. CRMs use sophisticated and realistic representations of cloud microphysical processes and can reasonably well resolve the time evolution, structure, and life cycles of clouds and cloud systems. CRMs also allow for explicit interaction between clouds, outgoing longwave (cooling) and incoming solar (heating) radiation, and ocean and land surface processes. Observations are required to initialize CRMs and to validate their results.The Goddard Cumulus Ensemble model (GCE) has been developed and improved at NASA/Goddard Space Flight Center over the past three decades. It is a multi-dimensional non-hydrostatic CRM that can simulate clouds and cloud systems in different environments. Early improvements and testing were presented in Tao and Simpson (1993) and Tao et al. (2003a). A review on the application of the GCE to the understanding of precipitation processes can be found in Simpson and Tao (1993) and Tao (2003). In this paper, recent model improvements (microphysics, radiation and land surface processes) are described along with their impact and performance on cloud and precipitation events in different geographic locations via comparisons with observations. In addition, recent advanced applications of the GCE are presented that include understanding the physical processes responsible for diurnal variation, examining the impact of aerosols (cloud condensation nuclei or CCN and ice nuclei or IN) on precipitation processes, utilizing a satellite simulator to improve the microphysics, providing better simulations for satellite-derived latent heating retrieval, and coupling with a general circulation model to improve the representation of precipitation processes. Future research is also discussed. Published by Elsevier B.V.

Assimilation of remotely sensed precipitation observations into numerical weather prediction models can improve precipitation forecasts and extend prediction capabilities in hydrological applications. This paper presents a new regional ensemble data assimilation system that assimilates precipitation-affected microwave radiances into the Weather Research and Forecasting Model (WRF). To meet the challenges in satellite data assimilation involving cloud and precipitation processes, hydrometeors produced by the cloud-resolving model are included as control variables and ensemble forecasts are used to estimate flow-dependent background error covariance. Two assimilation experiments have been conducted using precipitation-affected radiances from passive microwave sensors: one for a tropical storm after landfall and the other for a heavy rain event in the southeastern United States. The experiments examined the propagation of information in observed radiances via flow-dependent background error auto-and cross covariance, as well as the error statistics of observational radiance. The results show that ensemble assimilation of precipitation-affected radiances improves the quality of precipitation analyses in terms of spatial distribution and intensity in accumulated surface rainfall, as verified by independent ground-based precipitation observations.

CORRESPONDING AUTHOR: Toshihisa Matsui, Code 612, NASA Goddard Space Flight Center, Greenbelt, MD 20771, E-mail: Toshihisa.Matsui-1@nasa.gov

Previous observational studies have identified three different types of diurnal precipitation variation over the conterminous U.S.: localized afternoon rainfall maxima over the Mississippi and Ohio valleys, propagating mesoscale convective systems (MCSs) from the Rocky Mountain region, and propagating MCSs over the Appalachian Mountains. This study focuses on the second type, which involves nocturnal rainfall maxima from eastward‐propagating MCSs on the lee side of the Rocky Mountains. This study evaluates model simulations with regard to rainfall using observations and assesses the impact of microphysics, surface fluxes, radiation, and terrain on the simulated diurnal rainfall variation. A regional high‐resolution model was used to conduct a series of real‐time forecasts during the Midlatitude Continental Convective Clouds Experiment (MC3E) in 2011 over the Southern Great Plains. The model ably captured most heavy precipitation events. When all forecast days are composited, the mean forecast depicts accurate, propagating precipitation features and thus the overall diurnal variation. However, individual forecasts tend to overestimate the rainfall for light precipitation events, have location errors, and misrepresent convection in some cases. A post mission case study is performed on one multi‐cell, eastward‐propagating MCS event; the results suggest that cold‐pool dynamics were an important physical process. Model results also indicate that terrain effects are important during the initial stages of MCS development. By increasing the terrain height by 10%, the simulated rainfall is increased and in better agreement with observations. On the other hand, surface fluxes, and radiation processes only have a secondary effect for short‐term simulations.

Precipitation affects many aspects of our everyday life. It is the primary source of freshwater and has significant socioeconomic impacts resulting from natural hazards such as hurricanes, floods, droughts, and landslides. Fundamentally, precipitation is a critical component of the global water and energy cycle that governs the weather, climate, and ecological systems. Accurate and timely knowledge of when, where, and how much it rains or snows is essential for understanding how the Earth system functions and for improving the prediction of weather, climate, freshwater resources, and natural hazard events.The Global Precipitation Measurement (GPM) mission is an international satellite mission specifically designed to set a new standard for the measurement of precipitation from space and to provide a new generation of global rainfall and snowfall observations in all parts of the world every 3 h. The National Aeronautics and Space Administration (NASA) and the Japan Aerospace and Exploration Agency (JAXA) successfully launched the Core Observatory satellite on 28 February 2014 carrying advanced radar and radiometer systems to serve as a precipitation physics observatory. This will serve as a transfer standard for improving the accuracy and consistency of precipitation measurements from a constellation of research and operational satellites provided by a consortium of international partners. GPM will provide key measurements for understanding the global water and energy cycle in a changing climate as well as timely information useful for a range of regional and global societal applications such as numerical weather prediction, natural hazard monitoring, freshwater resource management, and crop forecasting.

The Global Precipitation Measurement (GPM) mission will advance the measurement of global precipitation, making possible high spatial resolution precipitation measurements. GPM will provide the first opportunity to calibrate measurements of global precipitation across tropical, mid-latitude, and polar regions. The GPM mission has the following scientific objectives: (1) Advance precipitation measurement capability from space through combined use of active and passive remote-sensing techniques; (2) Advance understanding of global water/energy cycle variability and fresh water availability; (3) Improve climate prediction by providing the foundation for better understanding of surface water fluxes, soil moisture storage, cloud/precipitation microphysics and latent heat release in the Earth's atmosphere; (4) Advance Numerical Weather Prediction (NWP) skills through more accurate and frequent measurements of instantaneous rain rates; and (5) Improve high impact natural hazard (flood/drought, landslide, and hurricane hazard) prediction capabilities. The GPM mission centers on the deployment of a Core Observatory carrying an advanced radar / radiometer system to measure precipitation from space and serve as a reference standard to unify precipitation measurements from a constellation of research and operational satellites. GPM, jointly led with the Japan Aerospace Exploration Agency (JAXA), involves a partnership with other international space agencies including the French Centre National d’Études Spatiales (CNES), the Indian Space Research Organisation (ISRO), the U.S. National Oceanic and Atmospheric Administration (NOAA), the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), and others. The GPM Core Observatory is currently being prepared for shipment to Japan for launch. Launch is scheduled for February 2014 from JAXA’s Tanegashima Space Center on an H-IIA 202 launch vehicle.

Two distinct snowfall events are observed over the region near the Great Lakes during 19–23 January 2007 under the intensive measurement campaign of the Canadian CloudSat/CALIPSO validation project (C3VP). These events are numerically investigated using the Weather Research and Forecasting model coupled with a spectral bin microphysics (WRF‐SBM) scheme that allows a smooth calculation of riming process by predicting the rimed mass fraction on snow aggregates. The fundamental structures of the observed two snowfall systems are distinctly characterized by a localized intense lake‐effect snowstorm in one case and a widely distributed moderate snowfall by the synoptic‐scale system in another case. Furthermore, the observed microphysical structures are distinguished by differences in bulk density of solid‐phase particles, which are probably linked to the presence or absence of supercooled droplets. The WRF‐SBM coupled with Goddard Satellite Data Simulator Unit (G‐SDSU) has successfully simulated these distinctive structures in the three‐dimensional weather prediction run with a horizontal resolution of 1 km. In particular, riming on snow aggregates by supercooled droplets is considered to be of importance in reproducing the specialized microphysical structures in the case studies. Additional sensitivity tests for the lake‐effect snowstorm case are conducted utilizing different planetary boundary layer (PBL) models or the same SBM but without the riming process. The PBL process has a large impact on determining the cloud microphysical structure of the lake‐effect snowstorm as well as the surface precipitation pattern, whereas the riming process has little influence on the surface precipitation because of the small height of the system.