School of Meteorology (Defense)

Limitations and Potential of Complex Cloud Analysis and Its Improvement for Radar Reflectivity Data Assimilation Using OSSEs

Chong-Chi Tong

OU School of Meteorology / Center for Analysis and Prediction of Storms

16 November 2015, 9:00 AM

National Weather Center, Room 4140
120 David L. Boren Blvd.
University of Oklahoma
Norman, OK

The effectiveness of the complex cloud analysis package in the Advanced Regional Prediction System (ARPS), usually applied in combination with the wind analysis using the ARPS three-dimensional variational (3DVAR) data assimilation, for assimilating reflectivity data for the purpose of improving short range precipitation forecasts has been demonstrated in many previous studies. Given its relatively low computational cost, it has been used in many realtime storm-scale forecasting applications.

In this dissertation, a general overview of various existing cloud analysis systems/algorithms is first provided, followed by a detailed introduction to the current (official) ARPS complex cloud analysis system. A new version of the hydrometeor analysis is implemented in the system, based on recently developed reflectivity operators that include a simple melting model. A hydrometeor classification algorithm based on polarimetric radar variables is used to help determine the hydrometeor species. The impact of the revised cloud analysis on very short range rainfall forecast is examined for a maritime mesoscale convective vortex case. Only small sensitivity of the results to the cloud analysis algorithms is found. Significant model error is likely to be a contributing factor.

To unambiguously determine the sensitivity of model forecasts to the cloud analysis procedure and to various treatments within, we focus the rest of our study on experiments conducted in an observing system simulation experiment (OSSE) framework, for a case of mesoscale convective system that occurred over central United States. A degraded initial condition is created by smoothing a truth forecast and by removing cloud fields. The simulation based on this degraded initial condition serves as a control, while sensitivity and data assimilation experiments try to improve the degraded initial conditions, or examine the impact of improved initial conditions.

The sensitivity of precipitation forecast of up to four hours to 1) model error due to the use of different microphysics scheme and 2) accuracy of model initial state variables is first investigated. The sensitivity to state variables is examined by inserting the perfect values of individual or a group of variables back into the smoothed initial conditions. The forecast winds, temperature (T), moisture (qv), total water-ice mixing ratio (qw), and radar reflectivity (Z) of sensitivity experiments are evaluated in terms of the root mean square (RMS) error calculated against the truth. The results show that compared to the initial state of hydrometeors, the model microphysics has a relatively small impact on the prediction of state variables in a relatively short range. However, microphysics errors become significant for longer range forecasts, such after two hours, when evaluated in terms of forecast reflectivity. Among the model state variables updated by the cloud analysis (i.e., potential temperature θ, moisture qv, and hydrometeor mixing ratio), qv is found to have the greatest impact on the prediction of state variables and forecast reflectivity. Precipitation hydrometeors have the second largest impact in terms of short-term prediction of qw and associated T while the importance of the non-precipitating hydrometeors is relatively small.

The other set of experiment is designed to examine the impact and effectiveness of the cloud analysis scheme. In these experiments, hydrometeor and associated in-cloud state variables in the initial condition are obtained using the ARPS cloud analysis scheme with varying configurations, rather than through direct insertion as in the first set of experiments. When performing the hydrometeor analysis only without updating any other in-cloud state variable, noticeable and long lasting (up to four hours) positive impact on forecast can be found in comparison with the hydrometeor-clear control. However, when qv is adjusted to the value of saturation mixing ratio, i.e., the relative humidity (RH) is adjusted to 100% within precipitation region, as is done in the current ARPS cloud analysis procedure, rapid forecast error growth is found in most state variables and reflectivity is significantly overforecast. The in-cloud temperature adjustment towards the moist-adiabat of low-level lifted parcel in the cloud analysis is found to work quite well.

Based on the results of the earlier OSSEs, efforts are made to improve the qv adjustment procedure in the cloud analysis to reduce precipitation over-forecast. The effectiveness of a better specified in-cloud humidity field, by direct insertion of the true RH (with analysis error contained), is firstly demonstrated. A modified qv adjustment procedure making use of the vertical velocity information is further proposed. This procedure avoids over-moistening in the downdraft regions, but the overall error in the adjusted qv is not necessarily reduced quantitatively due to loose relationship between vertical velocity and relative humidity. Still, the forecast resulting from the modified qv adjustment is improved over that from the original scheme. For predicting intense convection, the modified qv adjustment significantly outperforms the experiment with no in-cloud qv adjustment; however, there is under-prediction of precipitation.

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