A Physically Based Data QC Procedure and Its Impact on the Assimila9on of GPS RO Observa9ons in the Tropical Lower Troposphere

Transcription

1 A Physically Based Data QC Procedure and Its Impact on the Assimila9on of GPS RO Observa9ons in the Tropical Lower Troposphere Y.- H. Kuo, H. Liu, S. Sokolovskiy, Z. Zeng University Corpora/on for Atmospheric Research B. Ruston Naval Research Laboratory

2 Use of GPS RO Data in NWP Data quality control: Gross error check: Comparison with first guess (typical procedure for NWP) Observa/on based error check: Decisions are made based on actual observa/on quality Data trunca/on: Truncate data based on measurement uncertain/es Specifica/on of observa/on errors: Sta/s/cal observa/on error es/mates Observa/on- based error es/mates based on known uncertainty of observa/ons

3 Error characteriza9on and quality control (QC) of GPS RO bending angles (BA) Different occulta/ons have different observa/on errors Accurate specifica/on of BA observa/on error is only possible for spherically symmetric refrac/vity Large amount of water vapor makes tropospheric refrac/vity non- spherically symmetric, introducing uncertainty in BA Some parameters based on the structure of Wave Op/cs (WO) - transformed RO signals can be used as a proxy for the observa/on error and/or for trunca/on of BA profiles Assimila/on of each occulta/on with individual observa/on error or using zonally averaged error model

4 Dynamic (individual for each occ.) BA error estimation in the troposphere Gorbunov et al., JGR, 2006: relation of the RMS BA error to local spectral width (LSW) of WO-transformed signal. Depends on the definition of LSW. Following this approach with different definition of LSW based on the integral of local spectral power. Approach (1): piece-wise linear least squares fit Approach (2): fixed % of the integral BA from derivative of phase of WO-transformed signal Sliding spectrogram of WO-transformed signal Local width of the spectrogram. A proxy for RMS BA error x 2.

5 Structure of the proxy for BA RMS error (definition by least squares fit) The largest error in the low-latitude LT. Depends on humidity: largest over the oceans (not so large over Africa and Australia). 8km 5km 2km

6 Truncation of retrieved BA profile based on loss of coherence ("confidence parameter") Definition (1) CP = (P1 - P2) / P1 (%) where P1 ad P2 are powers of the 1st and 2nd max. local spectral components Definition (2) CP = P1 / PS (%) where PS is the sum of the powers for all spectral components Retrieved BA profiles can be discarded below height where either parameter 1 or parameter 2 reduces below some threshold.

7 Structure of the confidence parameter (1st definition) Smallest confidence in the low-latitude LT. Depends on humidity: smallest over the oceans (not so small over Africa and Australia). 8km 5km 2km

8 Distribu9on RO data with confidence parameter (SpagheR distribu/on for all RO profiles in Western Pacific, Sept. 8-10, 2008) CP = (P1- P2)/P1 (%), where P1 and P2 are powers of the 1st and 2nd maximum local spectral component. The RO data with CP < 30% are most located below 4km.

9 Distribu9on RO data with confidence parameter (CP < 30% at 2km, Sept. 8-10, 2008) The RO data with CP < 30% are most located over ocean and affected by moist convec/on

10 Distribu9on all available RO data (Sept. 8-10, 2008)

11 Rela9onship between CP and RO errors at 1.5 km in Tropics Comparison with ECMWF analysis Four months: July Sep April 2012 RO profiles within 1.5 h, 200 km within radiosonde

12 RO data comparison with ECMWF analyses (Sept. 2008) Height (km) Height (km) mean of (BA_occ BA_ech)/BA_ech CP00 CP30 CP40 CP Number of Profiles Height (km) STD of (BA_occ BA_ech)/BA_ech Experimental RO data with Radio- holographic (RH) filtering are used. Impact of the trunca/ons by the CP is mainly evident below 2km. This suggests that RO data above 2km are good afer applica/on of the RH filtering. Truncate GPS RO data based on confidence parameter (CP) changes BA biases rela/ve to ECMWF global analysis.

13 Assimilation experiments design WRF/DART cycling assimila/on 6- hourly during September 4-28, 2008 An experimental RO data set with Radio- holographic (RH) filtering and a relaxed trunca/on in lower troposphere RO bending angle data between 0-10km is assimilated RO bending angle data is custom- filtered consistent with WRF ver/cal grid (Liu et al., 2014a) The CDAAC local bending angle forward operator is used 6h- forecasts are verified to RO and radiosonde observa/ons over Western Pacific (120E 165E, 0-30N) over the assimila/on period A set of assimila/on experiments are performed: CP00 run: Radiosondes, cloud winds, aircraf data, land and ship surface pressure data, SATEM, and RO bending angle data; No CP check CP30 run: Same as CP00 run but reject RO data with CP < 30% below 2km CP40 run: Same as CP00 run but reject RO data with CP < 40% below 2km CP50 run: Same as CP00 run but reject RO data with CP < 50% below 2km

14 6h- Forecasts fits to RO refrac9vity data (CP checks below 2km)

15 Radiosonde used for verifica9on of 6h forecasts

16 6h- Forecasts fits to radiosonde water vapor (CP checks below 2km) Biases of CP30, CP40, and CP50 are closer to CP00 RMSEs of CP30, CP40, and CP50 are all smaller than CP00

17 6h- Forecasts fits to radiosonde water vapor CP30N includes a new shallow convec/on scheme from YSU. Otherwise, everything is the same as CP30 Biases of CP30 is largely removed by the new shallow convec/on scheme GPS RO nega/ve N bias may be par/ally alributable to model errors

18 Impact on WRF forecast of 2008 Typhoons 72- hour forecasts are ini/alized from the ensemble mean analyses every 6 hours star/ng from the genesis of the storms un/l their 1 st landing: Sinlaku: Z 13.00Z ( 18 forecasts) Hagupit: Z 24.00Z (19 forecasts) Jangmi: Z 28.00Z (15 forecasts) Total 52 X 72- hour forecasts are used for typhoon track forecast verifica/on 18

19 Impact on track forecast of 2008 Typhoons 72- h track forecast errors, averaged over 52 forecasts for the three typhoons (Sinlaku, Hagupit, Jangmi): CP30 has the smallest track error, CP50 the largest. CP00: 119.6km; ~ 5% reduc/on CP30: 114 km Data trunca/on using CP is useful, and there is an op/mal choice (CP1 = 30%)

20 Summary and Conclusions Three CP (CP30, CP40, CP50) check thresholds (<30%, <40%, and <50%) of RO data below 2km are tested. RO data QC with CP check improves 6- h O- B sta/s/cs in the tropical lower troposphere. Among the three CP s, CP30 gives the biggest track forecast error reduc/on for the three tropical cyclones of 2008, by 5%. The result suggests that RO data QC with CP check improves the use of GPS RO data in the tropical lower troposphere. RO observa/on errors are related to CP parameter, sugges/ng that we can use CP as a parameter in observa/on error specifica/on. Acknowledgement: We Thank Naval Research Laboratory for Suppor9ng this Research.