The Use of GPS Radio Occultation Data for Tropical Cyclone Prediction Bill Kuo and Hui Liu UCAR

Outline Prediction of tropical cyclones: Current forecast skills Comparison of operational model performance for 2011 The NOAA Hurricane Forecast Improvement Project Impact of assimilation approaches on NCEP GFS hurricane forecast skills GPS radio occultation (RO) and COSMIC Assimilation of GPS RO data Impact on the tropical cyclone analysis and prediction Hurricane Ernesto (2006) Typhoon Morakot (2009) Outlook

Current capability of NHC Good track forecast improvements. Errors cut in half in 15 years. Skill of 48h forecast as good as 24-h forecast 10 years ago. No improvement in intensity forecast. Off by 2 categories 5-10% of the time.

Current capability of CWB in (km) Good improvement from 1994 to 1005. 24 hour forecast errors reduced by half in 10 years. However, the skill becomes somewhat stagnant after 2005. Does not keep track of intensity forecast.

2011 Preliminary NHC Verifications ECMWF and GFS did well (again). ECMWF beat consensus at longer ranges. TVCA beat FSSE. AEMI not as good as GFSI, even at 5 days. GFDL and HWRF middle of the pack. NGPS, CMC, UKMET trailed.

Summary of 2011 Results and Implications Deterministic ECMWF (~16km) and GFS (~21 km) performed very well for 2011 (similar to that of 2010). Resolution matters. ECMWF beat multi-model consensus at longer ranges. High resolution deterministic global forecast is very useful. Multi-model consensus (TVCA) beat the FSU super ensemble (FSSE). Lower-resolution GSF global ensemble (AEMI, ~63km) did not perform not as well as high-resolution deterministic GFS (~21km), even at 5 days. Again, resolution matters. Performance of operational regional models (GFDL, 48/16/8 km, and HWRF, 27/9 km) was not as good as ECMWF and GFS global models. They are in the middle of the pack. Why didn’t regional model do better? What should they be used for? NGPS, CMC, UKMET global models trailed the other global and regional models. Hurricane forecast skill is different from general synoptic forecast skill (e.g., 500 mb height).

NOAA Hurricane Forecast Improvement Project (HFIP) 10-Year Goals: Reduce average track error by 50% for day 1 through 5 Reduce average intensity error by 50% for day 1 through 5 Increase the probability of detection (POD) for rapid intensity change to 90% at Day 1 decreasing linearly to 60% at day 5, and decrease the false alarm ratio (FAR) for rapid intensity change to 10% for day 1 increasing linearly to 30% at day 5. The focus on rapid intensity change is the highest forecast challenge indentified by the National Hurricane Center. Extend the lead time for hurricane forecasts out to Day 7 (with accuracy of Day 5 forecasts in 2003). 5-Year Goals: Reduce track and intensity forecast errors by 20%

HFIP Hurricane Research Priorities Advancement in data assimilation (DA) technique Technique to maximize the usefulness of observations Identification and analyses of the sources of model error Physics advances Air-sea and BL processes Microphysical/aerosol/radiation processes Advanced model diagnostic techniques Analyses and forecast of vortex low-order wavenumber evolution Analyses and forecast of large-scale and hurricane environment evolution (e.g. shear/track) Development of high resolution ensembles Identify techniques to utilize ensemble members for improved intensity guidance

Data Assimilation Methods Pros Cons 3D-Var Inexpensive Practical approach for operational purposes Static (fixed) error covariance Limited performance 4D-Var Use of observations at appropriate time Flow-dependent error covariance Suitably balanced analysis due to explicit use of model dynamics & physics Treats observations simultaneously Can handle a wide range of scales simultaneously Can use all observations from frequently reporting stations (high temporal resolution) Demonstrated capability (better than 3D-Var) Expensive 4D-Var iterations are sequential – difficult to make full use of current computers & likely impossible in foreseeable future. Require TL/AD models – difficult to maintain up-to-date code EnKF Simple to design and code No AD or minimization is needed Provide estimates of error covariance (but noisy) Allows for the full history of the evolution of forecast errors. Can use the full model, including non-differentiable and stochastic terms. Explicit estimate of errors. Facilitates ensemble forecasting. Treats one observation at a time, cost increases with data volume Can handle limited range of scales (because of localization) Can only estimate mean and covariances from a small sample, assuming Gaussian distributions Is still experimental (comparable or slightly better than 3D-Var) Hybrid “Error of the day” for 3D-Var or 4D-Var can be obtained through ensemble Easy to implement Considered to be an attractive compromise In experimental phase Still expensive

Comparison of EnKF vs 3D-Var Same GFS model at T574L64 (~21km at 25N) EnKF: T254L64 (~47km at 25N) GSI (3D-Var): T574L64 (~21km at 25N) Use the same operational observational data stream 2010 North Hemisphere tropical cyclones Hamill et al (2011) MWR

Comparison of EnKF, 3D-Var, and Hybrid Deep layer (850-200 mb) vector wind, 26 July – 17 Sep, 2010, 20N-20S. Hamill et al. (2011)

Comparison of Ensemble Forecasts GFS/EnKF: T254L64, ~47km @25N NCEP Operational: T190L28, ~63km @25N ECMWF:T639L62, ~28km @ 25N CMC: 0.9 deg 28L UKMO:1.25x0.83, 38L

ECMWF and GFS Ensemble Forecast Comparison Comparison between ECMWF (T639L62, ~28km at 25N) and GFS/EnKF (T254L64, ~47km at 25N) ensemble prediction of tropical cyclones. ECMWF has significantly better performance over Western Pacific. The GFS/EnKF has larger errors over the Western Pacific than Eastern Pacific or Atlantic. Hamill et al. (2011)

Differences between ECMWF and GFS/EnKF for 200 mb wind, averaged from 7/26 – 9/27, 2010. From Hamill et al (2011). Larger difference over Western Pacific is noted.

Radio Occultation Technique COSMIC – Six-Satellite constellation mission, launched in April 2006

Characteristics of GPS RO Data Limb sounding geometry complementary to ground and space nadir viewing instruments Global coverage Profiles ionosphere, stratosphere and troposphere High accuracy (equivalent to <1 K; average accuracy <0.1 K) High precision (0.02-0.05 K) High vertical resolution (0.1 km near surface – 1 km tropopause) Only system from space to observe atmospheric boundary layer All weather-minimally affected by aerosols, clouds or precipitation Independent height and pressure Requires no first guess sounding No calibration required Independent of processing center Independent of mission No instrument drift No satellite-to-satellite bias Compact sensor, low power, low cost References: Ho et al., TAO, 2009, Anthes et al., BAMS, 2008 All of these characteristics have been demonstrated in peer-reviewed literature—many were proven by COSMIC 16

GPS RO observations advantages for weather analysis and prediction There are considerable uncertainties in global analyses over data void regions (e.g., where there are few or no radiosondes), despite the fact that most global analyses now make use of satellite observations. GPS RO missions (such as COSMIC) can be designed to have globally uniform distribution (not limited by oceans, or high topography). The accuracy of GPS RO is compatible or better than radiosonde, and can be used to calibrate other observing systems. GPS RO observations are of high vertical resolution and high accuracy. GPS RO is an active sensor, and provides information that other satellite observing systems could not provide GSP RO provide valuable information on the 3D distribution of moisture over the tropics, which is important for typhoon prediction.

Problems of using GPS RO data in weather models GPS RO data (e.g., phase, amplitude, bending angles, refractivity) are non-traditional meteorological measurements (e.g., wind, temperature, moisture, pressure), advanced assimilation systems are needed to effectively assimilate such observations. The limb-viewing geometry makes their measurement characteristics very different from in situ point measurements (e.g., radiosonde) or the nadir-viewing passive microwave/IR measurements. Advanced observation operators are needed. The GPS RO measurements are not perfect and subject to various sources of error (e.g., measurement noise, tracking errors, uncalibrated ionospheric effects, super-refraction,…etc). Model configuration and issues (e.g., domain size, model top, parallelization, regional/global, ionosphere, model errors) affect the choices of assimilation strategies and outcome.

GPS RO measurements & processing Φ1, Φ2, a1, a2 Phase and amplitude of L1 and L2 Radio holographic methods, taking multipaths into account Geometrical (single ray) w/o amplitudes Bending angles of L1 and L2 α1, α2 Satellites orbits & Spherical symmetry Bending angle (Neutral A.) α Refractivity N Ionospheric correction Bending angle optimization & Abel inversion T, e, P A priori meteorological data & 1D-Var

Observation type and operators Error Characteristics Complexity of Operator atmospheric Neutral Obs Type Obs Operator N. A. Retrieved T, e, P Interpolation in time & space Refractivity Local refractivity Nonlocal refractivity Nonlocal excess phase Bending angle Local bending angle 2D/3D Ray tracer Phase + Ray shooting (orbits) L1,L2 + Ionospheric & Plasmaspheric model Phase, amplitude A Priori information is needed Error structure is too complicated to properly characterize Possible choices Operator is too complicated for now

Data Assimilation Methods Pros Cons 3D-Var Inexpensive Practical approach for operational purposes Static (fixed) error covariance Limited performance 4D-Var Use of observations at appropriate time Flow-dependent error covariance Suitably balanced analysis due to explicit use of model dynamics & physics Treats observations simultaneously Can handle a wide range of scales simultaneously Can use all observations from frequently reporting stations (high temporal resolution) Demonstrated capability (better than 3D-Var) Expensive 4D-Var iterations are sequential – difficult to make full use of current computers & likely impossible in foreseeable future. Require TL/AD models – difficult to maintain up-to-date code EnKF Simple to design and code No AD or minimization is needed Provide estimates of error covariance (but noisy) Allows for the full history of the evolution of forecast errors. Can use the full model, including non-differentiable and stochastic terms. Explicit estimate of errors. Facilitates ensemble forecasting. Treats one observation at a time, cost increases with data volume Can handle limited range of scales (because of localization) Can only estimate mean and covariances from a small sample, assuming Gaussian distributions Is still experimental (comparable or slightly better than 3D-Var) Hybrid “Error of the day” for 3D-Var or 4D-Var can be obtained through ensemble Easy to implement Considered to be an attractive compromise In experimental phase Still expensive

Current DA use of GPS RO data 3D-Var 4D-Var EnKF Hybrid 2D Bending angle SSI ECMWF Local bending angle GSI WRF-Var ECMWF UKMO Meteo France Nonlocal excess phase WFR-Var GSI* WRF-DART Nonlocal refractivity JMA Local refractivity NCEP CWB Env. Canada JMA, CMA WRF-DA As of June 2008 (GRAS-SAF Workshop you attended), Meteo France did 4DVAR/local bending angle At that time, Environment Canada (EC) used 4DVAR/refractivity What is nonlocal refractivity? JMA used 4DVAR/refractivty in 2010. *Ma et al. (2011) atmospheric river study, using regional GSI + ARW. Under test

Impact of COSMIC on Hurricane Ernesto (2006)

Hurrican Ernesto: Formed: 25 August 2006 Reached Hurricane strength: 27 August Dissipated: 1 September 2006 15:50 UTC 27 August 2006 Picture taken by MODIS, 250 m resolution

Forecast experiments WRF/DART EnKF system 36-km, 32 member ensemble No GPS: initialized from AVN/GFS analysis at 2006-08-23-06Z GPS all: assimilate all 15 GPS profiles at 2006-08-23-06Z, followed by a 5-day forecast GPS 1 : only assimilate 1 GPS profile at 2006-08-23-06Z, followed by a 5-day forecast WRF/DART EnKF system 36-km, 32 member ensemble Use non-local excess phase observation operator

Low-level moisture change by assimilating GPS Assimilation of 1 GPS RO sounding in the vicinity of Hurricane Ernesto caused a 1.8g/kg increase in moisture at 1.5 km. This was enough to get the hurricane genesis going.

NCAR 4-Day Ernesto Forecasts The Actual Storm Forecast with GPS Forecast without GPS The next three charts show the progression of Ernesto over the 102 hours of the NCAR forecast.  Images on the left are actual satellite photos of the event.  Images on the right were synthesized from the forecast made without GPSRO data, while the images in the center are from the forecast that assimilated the 15 GPSRO profiles.  The elapsed time since the beginning of the forecast is shown on the left-hand images -- in this case, 6 and 30 hours. Y.-H. Kuo (NCAR), 2007

How does GPS RO data improve hurricane forecast?

WRF/DART ensemble assimilation of COSMIC GPSRO soundings WRF/DART ensemble Kalman filter data assimilation system 36-km, 32-members, 5-day assimilation Assimilation of 178 COSMIC GPSRO soundings (with nonlocal obs operator, Sokolovskiey et al) plus satellite cloud-drift winds Independent verification by ~100 dropsondes. 178 COSMIC GPSRO soundings during 21-26 August 2006 From Liu et al. (2011)

Distribution of GPS RO data

Experiment Design Name Conventional data GPS RO NODA No Continuous forecast ONLY Yes GPS only ONLY6km Yes above 6km GPS only above 6km CTRL Convectional RO Conventional + GPS RO6km Yes above 6 km Convectional + GPS6km > The first three experiments try to identify the impact of GPS data in a clean setting (without the use of any other data) > The second three experiments assess the impact of GPS RO data in a more realistic operational setting. > Conventional data includes radiosondes, surface, aircraft reports, satellite cloud track winds, no radiance.

Analysis of 850 hPa Wind and Total Column Cloud Water (06Z August 23, 36 hours before Ernesto’s genesis, GPSonly run) Where Ernesto formed at 0000 UTC 25 August Convergence and convection in Ernesto’s genesis area.

Daily Analysis Increments of Q and T (GPSonly run) (850 hPa, August 23, 2 days before Ernesto’s genesis)

2-hour Forecast Difference of GPSonly-NODA (700 hPa) Water vapor 06Z 23 August Wind 00Z 24 August 00Z 25 August Ernesto’s genesis time

Daily Analysis Increments of Wind (August 23, 2 days before Ernesto’s genesis, m/s) GPSonly run 250 hPa GPSonly>6km run 700 hPa Strong correlation between GPS in the lower troposphere and winds at all levels.

2-hour Wind Forecast Differences (250 hPa) GPSonly-FCST 06Z 23 August GPSonly>6km-FCST 00Z 24 August 00Z 25 August Ernesto’s genesis time

2-hour Forecasts RMS fit to Radiosondes and dropsondes (Averaged over tropical Atlantic, 21-26 August, 2006)

Assimilation Experiments of RO and Conventional Data CTRL (NO GPS) run: Assimilate conventional data. RO6km run: Add RO refractivity data only higher than 6km. RO run: Add RO refractivity data of all levels. Non-local RO refractivity operator is used. There is a question or debate on if the GPS data in the lower troposphere is helpful because of the bias of the GPS data in the lowest 2km. However, model also has large error in the lower troposphere.

Analyses of SLP and 1000 hPa Vorticity (00UTC 25 August) CTRL (NOGPS) RO6km RO

48h-forecast of Track errors and SLP Intensity CTRL GPS>6km Ensemble mean of 48-hour forecasts of Ernesto’s central sea level pressure (hPa, top) and track error (km, bottom) initialized from the analyses at 0000 UTC 25 August 2006 GPS

Total Integrated Water Vapor of 48-hour Forecasts (00UTC 25 August) IR image RO CTRL 24h FCST 48h FCST The next three charts show the progression of Ernesto over the 102 hours of the NCAR forecast.  Images on the left are actual satellite photos of the event.  Images on the right were synthesized from the forecast made without GPSRO data, while the images in the center are from the forecast that assimilated the 15 GPSRO profiles.  The elapsed time since the beginning of the forecast is shown on the left-hand images -- in this case, 6 and 30 hours.

48-hour Forecasts of Ernesto (2006) with Assimilation of GPS and Conventional Observations IR Image RO RO6km 24h FCST 48h FCST Assimilation of GPS > 6km shows less positive impact

Summary of Ernesto Study Assimilation of GPS RO data adds moisture in the lower troposphere, and produces noticeable wind increments, consistent with the convective environment. GPS RO data in the lower troposphere is crucial for creating a favorable environment for hurricane genesis Assimilation of GPS RO data produced improved analysis and subsequent forecasts both in terms of track and intensity.

Impact of GPS RO data on Typhoon Morakot (2009)

--- Objective analysis from ~450 automatic stations Observed Rainfall of Typhoon Morakot (2009) --- Objective analysis from ~450 automatic stations From August 6 to 10, 2009, Typhoon Morakot brought extraordinary rainfall over Taiwan, breaking 50 year’s precipitation record, causing a loss of more than 700 people and estimated property damage exceeding US$3.3 billion Maximum 24-h gauge value of 1700 mm (world record1825 mm) Maximum 96-h gauge value of 3739 mm at Chiayi County The spatial distribution of the objective analysis of the observed rainfall produced by Typhoon Morakot over Taiwan and surrounding islands (unit: mm): (a) 96-h accumulated rainfall on August 6-10; (b) 24-h accumulated rainfall on August 8-9. The white cross mark shows the maximum gauge value, 3739 mm in (a) and 1700 mm in (b). The box with black frame in (a) is defined as the verification target area, with latitudes from 22.50ºN to 23.74ºN, longitudes from 120.32ºE to 121.14ºE (about 130×90 square km). (a) (b)

Assimilation experiments for Morakot Assimilation from 00Z August 3 to 18Z August 8, 2009 with 2-hourly cycling. 36km analysis grid with 35 levels 48h Forecast with nest grids 36km/12km/4km starting at August 7 00Z. CWB observations are used CTL run: Assimilate CWB conventional observations GPS run: CTL run + COSMIC data.

Track analyses (August 3 06Z - 8 18Z) Shading: PV (in PVU) at low-level (9th model level ~ 800mb over ocean) Contour: Sea level pressure (in hPa) Hurricane symbol (white) is the observed cyclone position at that time (only after Aug. 25) EXC: is the experiment with GPS and satellite winds, CTL: is the experiment with only satellite winds Ensemble mean Observed NOGPS GPS

Track analyses errors (August 3 06Z - 8 18Z) Shading: PV (in PVU) at low-level (9th model level ~ 800mb over ocean) Contour: Sea level pressure (in hPa) Hurricane symbol (white) is the observed cyclone position at that time (only after Aug. 25) EXC: is the experiment with GPS and satellite winds, CTL: is the experiment with only satellite winds Ensemble mean Observed NOGPS GPS 48

SLP intensity analyses (August 3 06Z - 8 18Z) Shading: PV (in PVU) at low-level (9th model level ~ 800mb over ocean) Contour: Sea level pressure (in hPa) Hurricane symbol (white) is the observed cyclone position at that time (only after Aug. 25) EXC: is the experiment with GPS and satellite winds, CTL: is the experiment with only satellite winds Ensemble mean Observed NOGPS GPS 49

24h Rain forecast (August 7 - 8 00Z) Shading: PV (in PVU) at low-level (9th model level ~ 800mb over ocean) Contour: Sea level pressure (in hPa) Hurricane symbol (white) is the observed cyclone position at that time (only after Aug. 25) EXC: is the experiment with GPS and satellite winds, CTL: is the experiment with only satellite winds Ensemble mean Observed NOGPS GPS 50

48h Rain forecast (August 8 - 9 00Z) Shading: PV (in PVU) at low-level (9th model level ~ 800mb over ocean) Contour: Sea level pressure (in hPa) Hurricane symbol (white) is the observed cyclone position at that time (only after Aug. 25) EXC: is the experiment with GPS and satellite winds, CTL: is the experiment with only satellite winds Ensemble mean Observed NOGPS GPS 51

Rain Probability Forecast (August 7-8 00Z) Shading: PV (in PVU) at low-level (9th model level ~ 800mb over ocean) Contour: Sea level pressure (in hPa) Hurricane symbol (white) is the observed cyclone position at that time (only after Aug. 25) EXC: is the experiment with GPS and satellite winds, CTL: is the experiment with only satellite winds Ensemble mean Observed 52

Rain Probability Forecast (August 8 - 9 00Z) Shading: PV (in PVU) at low-level (9th model level ~ 800mb over ocean) Contour: Sea level pressure (in hPa) Hurricane symbol (white) is the observed cyclone position at that time (only after Aug. 25) EXC: is the experiment with GPS and satellite winds, CTL: is the experiment with only satellite winds Ensemble mean Observed 53

WRF/DART Analyses and Forecasts for Morakot (2009) Control (with GPS RO): CWB operational observations including TC BOGUS data, 6-hourly analysis cycle. NOGPS: remove the GPS RO data from the control run. Assimilations started 12UTC Aug. 3, 2009. 45km grid only for both assimilation and forecasts. CWB TWRF is also full cycled for the same period. Cost of the 16 ensemble forecasts is equivalent to one nested (45/15/3km) deterministic forecast.

Forecast initialized at 12 UTC 5 August No GPS With GPS

Forecasts initialized at 12 UTC 6 August No GPS With GPS

250 mb Wind Analyses Differences (12Z Aug 5)

850 mb Moisture Flux Analyses Differences (12Z Aug 5)

500 mb height Analyses Differences (12Z Aug 5)

NO GPS Difference GPS Two week assimilation, 1-14 June 2007, 850 mb wind field.

Impact of GPSRO data on Typhoon MORAKOT track forecast From 18 UTC 3 to 12 UTC 7 August 2009 with Sixteen 6-h WRFDA/WRF (TWRF) full cycles Typhoon WRF (TWRF) with CWB 45-km operational domain (222x128x45) Totally there are 506 GPSRO profiles available during the experimental period. With GPSRO data assimilated, mean error is reduced from 131.4 km to 114.9 km (14% improvement). From: Y.-R. Guo

Summary of Morakot Study Assimilation of the GPS data: Improve analysis and prediction of typhoon track Improve rainfall prediction Enhanced upper-level divergence, increased low-level moisture flux, and intensity 500 mb height Performance of WRF/DART is superior to WRF-Var Positive impact of GPSRO is also seen in WRF-Var

Typhoon Prediction During T-PARC Assimilation of GPSRO data from COSMIC improved the quality of regional analysis and typhoon track errors. For four storms in T-PARC 2008, the average improvement is 13%. 48-h of track forecast errors for Typhoon Jangmi (2008) with and without COSMIC. From H. Liu and Jeff Anderson

Data Density for COSMIC and COSMIC-II Options: A, B, C, and D - 6 x 72o COSMIC-IIA - 8 x 72o + 4 x 24o COSMIC-IIB - 12 x 72o COSMIC-IIC - 6 x 72o + 6 x 24o - 4 x 72o + 8 x 24o

COSMIC-II Soundings Geographic Coverage (6@72°, 6@24°) 1 hour 3 hour Geographic coverage of radio occultation soundings for COSMIC-II. GPS and Galileo transmitters assumed. Six COSMIC-II spacecraft at 72 deg inclination, and six spacecraft at 24 deg inclination. Orbits. Altitude 800km, circular Azimuth coverage of RO antenna, 60 deg off velocity vector 24 hour 6 hour

Summary GPSRO observations are not affected by clouds and precipitation GPSRO observations provide three-dimensional water vapor information over the tropics, which are crucial for: Tropical cyclone genesis Tropical cyclone intensity forecast Tropical cyclone track forecast Current FORMOSAT-3/COSMIC sounding distribution is NOT optimal for tropical prediction. The data density is the lowest over the tropics (0.4 sounding over 500 km x 500 km in one day). FORMOSAT-7/COSMIC-2, with 10 times more soundings will significantly improve the skills of tropical cyclone prediction: Improve track, intensity and rainfall forecast for typhoons affecting Taiwan Directly contribute to all the goals of NOAA’s Hurricane Forecast Improvement Project (HFIP)