Ice fog: T~<-10C RHi>100%

Transcription

1 SATELLITE AND RADIOMETER BASED NOWCASTING APPLICATIONS FOR ARCTIC REGIONS Ismail Gultepe 1, Mike Pavolonis 2, Victor Chung 3, Corey Calvert 4, James Gurka 5, Randolf Ware 6, Louis Garand 7 G. Toth Aug Location Larsen Sound (south of Prince of Wales Island, west of the Boothia Peninsula, north of King William Island and east of Gateshead Island). FOG DEFINITIONS T>0C RHw>100% Warm fog Freez. fog: T~>-10C RHw~100% Cold fog Ice fog: T~<-10C RHi>100% 1

2 FOG AND ICING ALGORITHMS USING GOES/MODIS OBSERVATIONS FRAM-YK (Yellowknife, Nov-2010/May-2011) [ice fog] USE GEM MODEL SIMULATIONS (E.G. SURFACE T, RH) USE GOES VIS, SW-IR (3.9 micron), IR (10.3 micron) CHANNELS, INCLUDING IR T APPLY DAY/NIGHT ALGORITHM (BECAUSE OF SW EFFECTS ON 3.9 MICRON CHANNEL) INTEGRATE ALL ABOVE FOR FINAL PRODUCTS SURFACE OBSERVATIONS AT THE SUPERSITE Yellowknife, NWT, Canada FRAM-YK (Yellowknife, Nov-2010/May-2011) [ice fog] 2

3 NOV 30 FREEZING FOG/DZ CASE DURING FRAM-ICE JAN 18 ICE FOG CASE DURING FRAM-ICE IF 3

4 ICE FOG CLOUD CEILING<3000 FT 4

5 SATELLITE APPLICATIONS GOES MODIS AVHRR VIIRS PCW (Future applications) GOES-R (Future applications) GOES-R ABI Bands Future GOES Imager (ABI) Band Wavelength Range (μm) Central Wavelength (μm) Sample Objective(s) Daytime aerosol-over-land, Color imagery Daytime clouds fog, insolation, winds Daytime vegetation & aerosol-over-water, winds Daytime cirrus cloud Daytime cloud water, snow Day land/cloud properties, particle size, vegetation Sfc. & cloud/fog at night, fire High-level atmospheric water vapor, winds, rainfall Mid-level atmospheric water vapor, winds, rainfall Lower-level water vapor, winds & SO Total water for stability, cloud phase, dust, SO Total ozone, turbulence, winds Surface properties, p low-level moisture & cloud Total water for SST, clouds, rainfall Total water & ash, SST Air temp & cloud heights and amounts 5

6 What Is FLS? Aviation-based fog/low stratus cloud definition VFR - Visual flight rules ceiling > 3000 ft (914 m) MVFR - Marginal visual flight rules 1000 ft (305 m) < ceiling < 3000 ft (914 m) IFR - Instrument flight rules 500 ft (152 m) < ceiling < 1000 ft (305 m) LIFR - Low instrument flight rules ceiling < 500 ft (152 m) The aviation flight rules above also include a visibility requirement, however, surface visibility is difficult to infer from satellites. Therefore, cloud ceiling is solely used to define fog and low stratus clouds. Impact of Satellite/NWP Fusion Gultepe et al (Mont.Weat.Rev.) With NWP RH as predictor (0.52) Without NWP as predictor (0.46) Traditional BTD technique (0.43) When boundary layer relative humidity information from the GFS is used as a predictor in the Bayes classifier, the maximum achievable skill score (Peirces s skill score) increases significantly 12 6

7 Barrow Notice how the traditional BTD FLS product would show the same signal (color) for both Barrow, Deadhorse, and Kaktovik Deadhorse Kaktovik MVFR Probability Barrow The GOES-R MVFR probability product indicates a < 50% probability of MVFR at Barrow and a > 50% probability of MVFR at Deadhorse and Kaktovik. In general, the GOES-R product is more sensitive than the BTD to localized changes in ceiling. Deadhorse Kaktovik 7

8 Yellowknife Yellowknife Yellowknife Yellowknife ABI channels 2, 7,14 Cloud Mask Cloud Phase Solar zenith angle Fused Fog/Low Cloud Detection Approach (future development) ABI VIIRS MVFR and IFR Probability Clear Sky RTM Output Data Fusion Using Naïve Bayesian Model -NWP Data (add additional fields -DEM -SST Data -Surface Emissivity Data 8

9 CONCLUSIONS 1. Quantitatively compare GOES-R products to FRAM field experiment measurements (these measurements are critical) 2. Incorporate additional NWP fields (e.g. wind) and mesoscale NWP models. 3. Work towards an all weather MVFR and IFR probability capability 4. Elevated temperature inversions can cause false alarms 5. For opaque clouds, the cloud phase information only pertains to the top most portion of the cloud 6. Use Bayesian fused satellite/model approach to develop a shortterm prognostic product (short-term predictions on fog formation and dissipation) 9