1. Byrd Polar Research Center, The Ohio State University, Columbus, OH 2 Atmospheric Sciences Program, Department of Geography, The Ohio State University, Columbus, OH 3 Israel Meteorological Service Funded by NSF and NASA Francis Otieno 1, David Bromwich 1, 2, Keith Hines 1 and Elad Shilo 3

2. Outline of Talk 1.Introduction and background 2.Objectives of this study 3.Model Domain and configuration 4.Results a)Data quality b)Surface variables c)Upper air variables 5.Work to be completed 6.Conclusion

3. Background Most contemporary forecast models have been developed primarily in and for the mid latitudes in the Northern Hemisphere. The Polar Meteorology Group optimized the Penn State NCAR MM5 model for polar applications. In tests over Greenland, SHEBA region, Atqasuk, Barrow and Iceland the model shows significant skill (e.g. Wilson 2010, Hines and Bromwich 2008, Bromwich et. al (2009) The Antarctic Mesoscale Prediction System (AMPS) project previously used Polar MM5 until June 2008 to make the forecasts but is currently using the new mesoscale model (Polar WRF, 3.0.1) AMPS archive probably provides the only detailed high resolution four dimensional structure of the Antarctic atmosphere; invaluable tools in providing forecasts needed in support of Antarctic operational activities.

4. Background In the 2009-2010 field season, 400 LC-130 missions were conducted by USAF on the continent (NSF 2010). Accurate forecasts have played a critical role in previous evacuations from the continent and will continue to do so as the number of research and tourist visits increase. The model is continuously becoming very sophisticated with new changes every year (PWRF2.0, 2.2, 3.0, 3.0.1.1, PWRF3.2 due Aug 2010) It is very important to periodically assess the skill of the model as the safety of personnel and economic operations in Antarctica depend on the accuracy these forecasts More information on Polar WRF (PMG Website)

5. Antarctic versus Arctic Antarctica lacks the dense data network needed to provide Polar WRF with an accurate representation of the large scale circulation. For skillful forecasts the model must accurately represent the Antarctic katabatic winds (Bromwich and Liu 1996, Parish and Bromwich 1998, Cassano and Parish 2000) that are governed by the balance of gravity, thermal stability and synoptic forcing (Elevated ice sheet) Sea ice impacts the atmosphere ocean interaction

6. Objectives Assess the performance of Polar WRF3.0.1.1 in short-term forecasts over Antarctica; On an annual time scale Identify an optimal configuration for short-term forecasts in Antarctica Identify any deficiencies in the model when used in Antarctica

7. The Domain Terrain-Radarsat Antarctic Mapping Project (RAMP-DEM Lui et al. 2001) SST – Real-time, global, sea surface temperature analyses from NCEP Sea Ice- SMMR on Nimbus 7 and SMM/I DMSP using bootstrapping (NSIDC) Lateral boundary Conditions from NCEP Final Analysis Reconstructed mean surface air temperature (Monaghan et al. 2008) provide the ice temperatures at depth. Fig. 1: Region of study showing the terrain elevation and location of stations. Red contour marks 0.1 fractional sea ice and shows the winter extent. All Upper air stations located near the coast (except Vostock and Amundsen-Scott) Not all stations useable

8. Model Physics and Configuration  WSM 5-class scheme  Goddard shortwave  RRTM Longwave (G)  Noah Land-surface model  Mellor Yamada-Janjic (Eta)  Grell-Devenyi  39 vertical levels  Based on Arctic Config. Polar WRF 3.0.1.1 (PWRF3.2 Aug 2010) Polar Stereographic centered at S. Pole 60 km; 120 x 120 grid size Forecast mode 48 hour run ; analyzed last 24 hrs SST updated every six hours Sea ice concentrations specified FNL lateral forcing every six hours

9. Observed Data  AWS Univ. Wisconsin Automatic Weather Project Surface temperature, pressure, wind and RH  NCDC Surface archive (overlap AWS)  BAS British Antarctic Survey (Radiosonde)  Upper Air soundings IGRA-Integrated Radiosonde Archive  BSRN Baseline Rad. Network –Downwelling LW and SW

10. DATA QUALITY Spikes in data not real Do not occur at the same time Very difficult to quality control wind information (spike may be an actual gust) Could have 98% availability for one variable and not the other Station may be a summer only Initially looked at 1993 (Madison 2009); excessive thinning Looking at 2007 additional AWS

11. DATA QUALITY Less than 50% availability Outliers 3σ, deviation from a running mean Automating QC is a difficult challenge Linear interpolation Just set to missing

12. Surface Pressure Model shows good skill (correlations > 0.9) for a number of stations There are problems with some locations with larger disparity between model and station elevation Not all stations have data for the whole period Some stations are not well represented e.g. Durmont D’Uvrille

13. January temperature correlations vary a lot; Results improve substantially with more rigorous quality control. The direction of flow has to be considered in the quality control; A coastal station may be more representative of a maritime rather than land depending on flow direction Some station reflect mostly local and not synoptic scale variability Temperature BiasRMSDCorr January (18) -2.13.40.61 July (25) -1.13.80.64

14. Results from the Peninsula Large differences in July days 1-5 and 20-25 Pressure variability simulated better than that of temperature At 60 km the model misses some of the island stations Three stations show similar SFCP behavior

15. Upper Air Results Annual range of temperature observations is captured well; range is larger near model top Polar WRF shows a summer warm bias in the upper troposphere (~300 hPa) Winter and summer temperature profiles are simulated well except at Marambio where model shows a warm bias.

16. TemperatureWind Speed BIASRMSDCORRBIASRMSDCORR 8501.042.570.941.425.440.69 7000.872.460.921.274.780.68 5000.031.770.960.514.950.77 4000.071.70.880.385.930.77 3000.92.010.65 - 0.246.50.80 2500.762.280.860.045.480.83 2000.041.960.930.454.190.86 Climatological features captured (colder, lower dew point temps-July; strong circumpolar winds Upper air annual statistics are from the nine IGRA stations Model wind speeds are slightly stronger than observed. U and V statistics suggest that upper level wind direction reasonably simulate

17. Geopotential heights simulated well in the lower and mid troposphere Differences get larger near the model top Possible fix (RRTMG)

18. Polar WRF 3.0.1.1 vs 9 IGRA Upper Air Stations for 2007 TemperatureGeopotentialWind SpeedU-WindV-Wind BIASRMSDCORRBIASRMSDCORRBIASRMSDCORRBIASRMSDCORRBIASRMSDCORR 8501.042.570.94-6.2623.670.961.425.440.690.245.390.690.455.340.65 7000.872.460.920.6326.170.891.274.780.680.964.900.69-1.766.580.57 5000.031.770.96-1.2729.560.970.514.950.770.836.070.67-0.146.430.69 4000.071.700.882.233.130.980.385.930.770.437.110.670.247.740.70 3000.902.010.655.3836.260.98-0.246.500.800.217.670.670.448.390.70 2500.762.280.8622.4741.30.980.045.480.830.086.730.680.397.470.70 2000.041.960.9336.9848.740.990.454.190.860.125.290.700.456.040.70 Biases in Polar WRF simulations are small between 850 and 300 hPa Geopotential errors become larger above 300 hPa (small percentage differences)

19. Downwelling Radiation SW and LW

20. The variability and amplitude of the downwelling longwave radiation (responsible for surface heating during the long Antarctic winters) is forecast well But the Model allows excessive downwelling shortwave (~40 Wm -2 ) at the surface during the Austral summer The excess shortwave appears in the hours following the local noon

21. Wind speed ANNDJFMAMJJASON BIASRMSDCORRBIASRMSDCORRBIASRMSDCORRBIASRMSDCORRBIASRMSDCORR 8501.45.40.690.43.60.831.75.10.741.86.10.651.05.40.7 7001.34.80.680.12.80.641.24.50.681.55.20.670.44.20.72 5000.55.00.770.53.50.810.55.20.760.45.30.75-0.14.20.81 4000.45.90.770.74.70.800.36.00.780.46.20.77-0.55.30.8 300-0.26.50.80.26.30.80-0.26.70.81-0.26.40.81-0.85.80.82 2500.05.50.830.14.50.861.15.80.81-0.55.40.84-0.95.20.85 2000.54.20.860.52.70.891.54.10.84-0.34.70.830.14.00.88 Meridional 8500.55.30.650.53.60.740.65.20.690.95.90.620.24.90.62 700-1.86.60.570.23.60.77-0.94.70.83-2.87.60.56-0.24.30.81 500-0.16.40.690.04.70.670.55.80.79-0.57.70.64-0.65.70.69 4000.27.70.7-0.26.30.670.97.10.78-0.29.00.640.26.60.7 3000.48.40.7-0.97.60.751.37.40.770.19.70.630.77.30.71 2500.47.50.7-0.55.70.740.96.60.730.38.90.640.76.60.72 2000.56.00.7-0.24.40.710.65.10.740.97.50.650.55.30.72 Zonal 8500.25.40.690.13.70.840.75.10.71-0.16.00.66-0.35.20.74 7001.04.90.690.63.80.851.95.10.710.75.20.680.14.40.83 5000.86.10.670.14.50.841.45.50.690.96.60.671.06.50.62 4000.47.10.67-0.25.60.860.86.50.70.57.60.660.67.70.62 3000.27.70.67-0.26.40.870.87.20.70.18.10.650.58.20.63 2500.16.70.68-0.35.10.871.16.40.72-0.37.20.680.37.30.63 2000.15.30.7-0.23.50.881.24.80.75-0.46.00.680.45.70.64 Little variation in seasonal WSP statistics No obvious height dependence Bias in wind components generally small (~1 m/s) Infer that wind direction probably okay DJF Zonal wind statistics slightly better (strong; JJA?)

22. Summary A number of other factors could influence the forecast performance of Polar WRF e.g. – Lateral boundary data (ERA-40, ERA-INTERIM, NNR or FNL – Interannual variability 1993 versus 2007 – Model physics and configuration (WRFV2.0 to WRF3.2) For stations that have been adequately scrubbed, the preliminary surface verification show good skill ( surface data are more problematic) Upper air statistics don’t show any major problems (300 hPa and at the model top) Overall Polar WRF shows skill levels in the SH that are comparable to those in the NH TBD Observations– Scrub and repeat; Only use AWS data from AMRC for overlaps with NCDC TBD –ModelMay ultimately use Polar WRF3.2 Bracket the impacts of interannual variability and lateral boundary data