COMPARISONS OF NOWCASTING TECHNIQUES FOR OCEANIC CONVECTION Huaqing Cai, Cathy Kessinger, Nancy Rehak, Daniel Megenhardt and Matthias Steiner National Center for Atmospheric Research Boulder, CO 14 th Conference on Aviation, Range, and Aerospace Meteorology Atlanta, GA January, 2010 ACKNOWLEDGMENTS This study is supported by NASA ROSES and NASA ASAP program and in collaboration with NRL and MIT LL

Oceanic Diagnosis and Nowcasting System Convective Diagnosis Oceanic (CDO) identifies convective cells CDO Interest CDO Binary Product Convective Nowcasting Oceanic (CNO-Titan) makes 1-hr and 2-hr nowcasts of storm location using an object tracker (Titan) CNO- Titan Nowcast CNO-Gridded produces gridded nowcasts that will more closely resemble storm structures CNO- Gridded Nowcast CNO-RF Random Forest Nowcast CNO-RF utilizes environmental and model-based inputs to better predict storm initiation and decay [Cai et al. (2009)] CTopCClassGCD With Growth/Decay Without Growth/Decay

CNO Based on TITAN (Dixon and Wiener, 1993) TITAN for Radar Data An Example of 1 Hr CNO-TITAN *1 hr nowcast of CDO valid at 1315 UTC on August 19, 2007 using TITAN technique is shown on the right; red lines on the right represent CDO = 2.5 verification. *Advantages of TITAN: computationally efficient; capability of addressing growth/decay. *Disadvantages of TITAN: polygons can only roughly represent storm shapes; tends to over- forecasting

CNO Based on Modified TITAN---- Gridded Forecast An Example of 1 Hr CNO-Gridded Forecast TITAN Motion Vectors at t 0 Gridded 0 hr TITAN Motion Vectors Temporal & Spatial Smoothing min Motion Vectors min Forecasts by Advecting Original Satellite Data at t 0 Gridded 1 hr TITAN Motion Vectors Gridded 2 hr TITAN Motion Vectors Merged with GFS Winds Closest in Time Temporal & Spatial Smoothing Temporal & Spatial Smoothing min Motion Vectors min Motion Vectors min Forecasts by Advecting 60 min Nowcasts min Forecasts by Advecting 120 min Nowcasts Merged with GFS Winds Closest in Time 1-3 Hr CNO-Gridded Forecast Flow Chart *Advantages of CNO-Gridded: realistic looking storms; low bias. *Disadvantages of CNO-Gridded: could be computationally expensive; no explicit growth/decay capability

CNO Based on Random Forest Statistical Analysis and Data Fusion The random forest technique produces an ensemble of decision trees from labeled training instances –during training, RF generates estimates of predictor importance –RF trees “vote” on classification of new data points, comprising a nonlinear empirical model that provides both deterministic predictions and probabilistic information Vote: 1 => 40 votes for “0”, 60 votes for “1”; consensus category “1” Data pt. Tree 1 Vote: 0 Data pt. Tree 2 Vote: 0 Data pt. Tree 3 Vote: 1 Data pt. Tree 4 Vote: 0 Data pt. Tree 100 … For details of RF technique in CoSPA R&D effort, referred to paper J10.4 by Ahijevych et al. on Thursday *Slide courtesy of John Williams and Dave Ahijevych

An Example of CNO- RF Forecast Compared with CNO- TITAN ( 1 hr) *1 hr forecasts valid at 1315 UTC on August 19, 2007 for both techniques; Red lines represent CDO = 2.5 verification *Advantages of random forest technique: more realistic looking storms; taking into account of storm environment to address storm growth/decay. *As a relatively new, novel technique for nowcasting, its potential needs to be fully explored CNO Hurricane Dean A B C D A B C D CNO-RF CNO-TITAN

Statistical Evaluation of the Three Nowcasting Techniques CSIBIAS 5 days of data from Aug 19-23, 2007 over the Gulf of Mexico domain are used to calculate the statistics with a grid size of ~ 5 km and CDO threshold of 2.5 All three techniques show skill over persistence RF and gridded forecast perform best at 1 hr lead time TITAN is the best at 2-3 hr lead time Gridded forecast is the best for 4-6 hr lead time

Relative Skill Comparisons of Three Nowcasting Techniques versus Persistence Gridded and RF nowcasts ~10 % better than persistence at 1 hr lead time TITAN is the best for 2 and 3 hr lead time (~20-30% improvement) Gridded nowcasts the best for 4,5 and 6 hr lead time (~ 15-25% improvement) Overall, gridded technique seems to be the best performer

Examples of 1 hr Gridded Forecast over the Gulf of Mexico Domain * White lines are CDO=2.5 verification, satellite data available every 30 min A B C D Issue time: 1215 UTC 2009/09/05 Valid time: 1315 UTC 2009/09/05 1 HR

Examples of 3 hr Gridded Forecast over the Gulf of Mexico Domain *White lines are CDO=2.5 verification, satellite data available every 30 min A B C D Issue time: 1215 UTC 2009/09/05 Valid time: 1515 UTC 2009/09/05 3 HR

Examples of 6 hr Gridded Forecast over the Gulf of Mexico Domain *White lines are CDO=2.5 verification, satellite data available every 30 min A B C D Issue time: 1215 UTC 2009/09/05 Valid time: 1815 UTC 2009/09/05 6 HR

Examples of 3 hr CDO Forecasts Based on CNO- Gridded Technique in the West Pacific Domain *White lines are CDO=1.5 verification; Cloud class is not used in CDO *Satellite data are available every 3 hrs A B C D Issue time: 2100 UTC 2009/12/28 Valid time: 0000 UTC 2009/12/29 3 HR

Examples of 3 hr CDO Forecasts Based on CNO- Gridded Technique in the West Pacific Domain *White lines are CDO=1.5 verification; Cloud class is not used in CDO *Satellite data are available every 3 hrs A B C D Issue time: 0000 UTC 2009/12/29 Valid time: 0300 UTC 2009/12/29 3 HR

Examples of 3 hr CDO Forecasts Based on CNO- Gridded Technique in the West Pacific Domain *White lines are CDO=1.5 verification; Cloud class is not used in CDO *Satellite data are available every 3 hrs A B C D Issue time: 0300 UTC 2009/12/29 Valid time: 0600 UTC 2009/12/29 3 HR

Summary Statistics of CNO-Gridded Forecasts 30 days of data from Sep 1-30, 2009 over the Gulf of Mexico domain are used to calculate the statistics with a grid size of ~ 5 km and CDO threshold of 2.5 The results showed here could serve as benchmark performance of extrapolation-based nowcasting techniques for oceanic convection Similar verification for model forecasts need to be done so that a comparison of convective forecasting skills between model and extrapolation can be obtained The black squares are statistics from Aug 19-22, 2007 What are the GFS model scores for oceanic convection???

Summary and Future Work Three nowcasting techniques (CNO-TITAN, CNO-Gridded and CNO- Random Forest) for oceanic convection forecasting in the 1-6 hr time frame are implemented, tested and compared in the Gulf of Mexico domain At 1 hr lead time, CNO-Gridded and CNO-RF got the best performance scores At 2-3 hr forecast lead time, CNO-TITAN outperforms the other techniques After 3 hr, CNO-Gridded outperforms the other techniques Based on the overall performance statistics, CNO-Gridded forecasts for 1-8 hr are implemented in the Gulf of Mexico and Pacific domain in the realtime Oceanic Diagnosis and Nowcasting System The summary statistical performance of CNO-Gridded extrapolating technique could serve as benchmark for future blending work of GFS model and extrapolating for oceanic convection in 1-8 hr time frame

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