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Thomas A. Jones, David J. Stensrud, Patrick Minnis, and Rabindra Palikonda


Assimilating satellite-retrieved cloud properties into storm-scale models has received limited attention despite its potential to provide a wide array of information to a model analysis. Available retrievals include cloud water path (CWP), which represents the amount of cloud water and cloud ice present in an integrated column, and cloud-top and cloud-base pressures, which represent the top and bottom pressure levels of the cloud layers, respectively. These interrelated data are assimilated into an Advanced Research Weather Research and Forecasting Model (ARW-WRF) 40-member ensemble with 3-km grid spacing using the Data Assimilation Research Testbed (DART) ensemble Kalman filter. A new CWP forward operator combines the satellite-derived cloud information with similar variables generated by WRF. This approach is tested using a severe weather event on 10 May 2010. One experiment only assimilates conventional (CONV) observations, while the second assimilates the identical conventional observations and the satellite-derived CWP (PATH).

Comparison of the CWP observations at 2045 UTC to CONV and PATH analyses shows that PATH has an improved representation of both the magnitude and spatial orientation of CWP compared to CONV. Assimilating CWP acts both to suppress convection in the model where none is present in satellite data and to encourage convection where it is observed. Oklahoma Mesonet observations of downward shortwave flux at 2100 UTC indicate that PATH reduces the root-mean-square difference errors in downward shortwave flux by 75 W m−2 compared to CONV. Reduction in model error is generally maximized during the initial 30-min forecast period with the impact of CWP observations decreasing for longer forecast times.

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Thomas A. Jones, David Stensrud, Louis Wicker, Patrick Minnis, and Rabindra Palikonda


Assimilating high-resolution radar reflectivity and radial velocity into convection-permitting numerical weather prediction models has proven to be an important tool for improving forecast skill of convection. The use of satellite data for the application is much less well understood, only recently receiving significant attention. Since both radar and satellite data provide independent information, combing these two sources of data in a robust manner potentially represents the future of high-resolution data assimilation. This research combines Geostationary Operational Environmental Satellite 13 (GOES-13) cloud water path (CWP) retrievals with Weather Surveillance Radar-1988 Doppler (WSR-88D) reflectivity and radial velocity to examine the impacts of assimilating each for a severe weather event occurring in Oklahoma on 24 May 2011. Data are assimilated into a 3-km model using an ensemble adjustment Kalman filter approach with 36 members over a 2-h assimilation window between 1800 and 2000 UTC. Forecasts are then generated for 90 min at 5-min intervals starting at 1930 and 2000 UTC. Results show that both satellite and radar data are able to initiate convection, but that assimilating both spins up a storm much faster. Assimilating CWP also performs well at suppressing spurious precipitation and cloud cover in the model as well as capturing the anvil characteristics of developed storms. Radar data are most effective at resolving the 3D characteristics of the core convection. Assimilating both satellite and radar data generally resulted in the best model analysis and most skillful forecast for this event.

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Thomas A. Jones, Patrick Skinner, Nusrat Yussouf, Kent Knopfmeier, Anthony Reinhart, Xuguang Wang, Kristopher Bedka, William Smith Jr., and Rabindra Palikonda


The increasing maturity of the Warn-on-Forecast System (WoFS) coupled with the now operational GOES-16 satellite allows for the first time a comprehensive analysis of the relative impacts of assimilating GOES-16 all-sky 6.2-, 6.9-, and 7.3-μm channel radiances compared to other radar and satellite observations. The WoFS relies on cloud property retrievals such as cloud water path, which have been proven to increase forecast skill compared to only assimilating radar data and other conventional observations. The impacts of assimilating clear-sky radiances have also been explored and shown to provide useful information on midtropospheric moisture content in the near-storm environment. Assimilation of all-sky radiances adds a layer of complexity and is tested to determine its effectiveness across four events occurring in the spring and summer of 2019. Qualitative and object-based verification of severe weather and the near-storm environment are used to assess the impact of assimilating all-sky radiances compared to the current model configuration. We focus our study through the entire WoFS analysis and forecasting cycle (1900–0600 UTC, daily) so that the impacts throughout the evolution of convection from initiation to large upscale growth can be assessed. Overall, assimilating satellite data improves forecasts relative to radar-only assimilation experiments. The retrieval method with clear-sky radiances performs best overall, but assimilating all-sky radiances does have very positive impacts in certain conditions. In particular, all-sky radiance assimilation improved convective initiation forecast of severe storms in several instances. This work represents an initial attempt at assimilating all-sky radiances into the WoFS and additional research is ongoing to further improve forecast skill.

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Johannes Mohrmann, Christopher S. Bretherton, Isabel L. McCoy, Jeremy McGibbon, Robert Wood, Virendra Ghate, Bruce Albrecht, Mampi Sarkar, Paquita Zuidema, and Rabindra Palikonda


Flight data from the Cloud System Evolution over the Trades (CSET) campaign over the Pacific stratocumulus-to-cumulus transition are organized into 18 Lagrangian cases suitable for study and future modeling, made possible by the use of a track-and-resample flight strategy. Analysis of these cases shows that 2-day Lagrangian coherence of long-lived species (CO and O3) is high (r = 0.93 and 0.73, respectively), but that of subcloud aerosol, MBL depth, and cloud properties is limited. Although they span a wide range in meteorological conditions, most sampled air masses show a clear transition when considering 2-day changes in cloudiness (−31% averaged over all cases), MBL depth (+560 m), estimated inversion strength (EIS; −2.2 K), and decoupling, agreeing with previous satellite studies and theory. Changes in precipitation and droplet number were less consistent. The aircraft-based analysis is augmented by geostationary satellite retrievals and reanalysis data along Lagrangian trajectories between aircraft sampling times, documenting the evolution of cloud fraction, cloud droplet number concentration, EIS, and MBL depth. An expanded trajectory set spanning the summer of 2015 is used to show that the CSET-sampled air masses were representative of the season, with respect to EIS and cloud fraction. Two Lagrangian case studies attractive for future modeling are presented with aircraft and satellite data. The first features a clear Sc–Cu transition involving MBL deepening and decoupling with decreasing cloud fraction, and the second undergoes a much slower cloud evolution despite a greater initial depth and decoupling state. Potential causes for the differences in evolution are explored, including free-tropospheric humidity, subsidence, surface fluxes, and microphysics.

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