Impact of FORMOSAT-3/COSMIC radio occultation data on the prediction of super cyclone Gonu (2007): a case study
This study aims to present an encouraging example of prediction of super cyclone Gonu over the northern Indian Ocean in 2007. A series of experiments are conducted using the advanced Weather Research and Forecasting model with three-dimensional variational method to assimilate GPS RO refractivity from FORMOSAT-3/COSMIC (hereafter referred as GPS) and radiosonde sounding (GTS) to highlight the relative impact of GPS RO data on model prediction. Significant differences in cyclone track and intensity prediction are exhibited in various experiments with and without cyclic assimilations. Both cold-start (non-cyclic) and hot-start (cyclic) runs with GPS RO data exhibit improvement on later track prediction compared to the control run without data assimilation. GPS experiment outperforms other experiments including GTS in track prediction with the smallest cross-track error. Sensitivity tests were also conducted to identify which GPS RO sounding gives more impact on track prediction. We found that the sounding closest to the cyclone exhibits the largest contribution to track prediction. Assimilation of the RO soundings in the vicinity of Gonu cyclone appears to modify the environmental conditions that result in a later development of a couplet of high and low pressure, leading to a positive impact on track prediction. Sensitivity experiments indicate that the initial information retrieved by GPS data at upper levels that produce colder temperature increments indeed contributes more improvement to track prediction.
KeywordsGPS radio occultation COSMIC WRF model 3DVAR Super cyclone Gonu
Tropical cyclones are the most devastating weather phenomena, and the prediction of tropical cyclones is a challenging issue for weather forecasters. A major problem of tropical cyclone prediction arises due to sparseness of the observations over the oceans. As a result, the initial conditions for a forecast model do not realistically depict the true state of the atmosphere. Initial conditions can be improved by making use of satellite data, e.g., satellite–based rain gauge measurements of rainfall through a physical initialization procedure (e.g., Krishnamurti et al. 1993, 1995). Apart from those data sets, another important source of data is contributed by GPS RO observations. FORMOSAT-3/COSMIC (Constellation Observing Systems for Meteorology, Ionosphere and Climate) is an important international satellite project in this regard to provide global sounding measurement. The complete description and scientific impacts of COSMIC have been given by Anthes et al. (2008). The GPS RO measurement plays a robust role in characterizing planetary atmospheres since the 1960s (Kliore et al. 1964), because it possesses beneficial features of uniform data coverage on the globe, no instrument drifting, high vertical resolution (up to few meters) and little affected by clouds, precipitation and aerosols (Kursinski et al. 2000). GPS-emitted ray can propagate through severe weather systems like heavy rain, cyclones and typhoons without contamination of the signal, whereas microwave radiometers are somewhat affected by clouds and light rain, and infrared (IR) radiometers are contaminated as well by clouds and aerosols (Kursinski et al. 1997). The coverage of radiosondes is rather uneven over the globe and in particular is very poor over the oceans. Furthermore, its temporal resolution is also fairly low (operationally 12-h frequency).
Various model experiments have shown that GPS RO soundings exhibit positive impacts on global and regional weather predictions (Kuo et al. 1997, 2000; Zou et al. 1999, 2000; Liu and Zou 2003; Healy et al. 2005; Healy and Thepaut 2006; Cucurull et al. 2006; Huang et al. 2007, 2010; Healy 2008; Poli et al. 2008, 2010; Cucurull 2010). In particular, typhoon forecasts were improved as well when GPS RO refractivity was assimilated into models (Huang et al. 2005, 2010; Chen et al. 2009; Kueh et al. 2009). We note that the above typhoon case studies have not detailed the processes of individual RO sounding and identify which vertical level is contributing to the improvement of forecasts.
The impact of FORMOSAT-3/COSMIC RO refractivity on the prediction of the super cyclone Gonu (2007) has been briefly introduced in Huang et al. (2010). In this study, we elaborate this case to identify and highlight RO data impacts. We explore the importance of specific GPS RO soundings, the mechanism for the improved track predication and their contributions from different vertical heights. Analyses of initial changes due to assimilation of GPS RO observations are conducted. The improvement in track prediction has been analyzed and explained in this study. In this context, we have found several important and essential aspects by conducting various sensitivity experiments. We highlight the importance of upper tropospheric temperature information in driving the cyclone track.
The manuscript is organized as follows. The simulated case and experimental design are given in Sect. 2. The simulation results, including initial analyses as well as track and intensity predictions, are discussed in Sect. 3 with verification against observations and global analysis. We provide possible mechanisms of predicted tracks as degraded or improved by the assimilated observations. The impacts of GPS RO data are further highlighted by sensitivity tests in Sect. 4. Finally, conclusions are given in Sect. 5.
2 The case and numerical experiments
2.1 Data sets
FORMOSAT-3/COSMIC (GPS), GTS and NCEP Aviation Model (AVN) global analysis data sets have been used to analyze the cyclone Gonu. The GTS data set from NCAR contains conventional radiosondes and synoptic surface observations. FORMOSAT-3/COSMIC has been able to provide around 2000 RO soundings per day over the globe since launch (Anthes 2011). NCEP-AVN global analysis at 1° × 1° resolution is used to provide initial first guess and boundary conditions for the three-dimensional variational data assimilation (3DVAR) of WRF version 2.2 in this study.
2.2 Cyclone Gonu
Gonu was the first-ever super cyclone formed over the Arabian Sea and was the strongest tropical cyclone on record in the Arabian Sea. This motivates the authors to choose this cyclone Gonu. Intense cyclones, like Gonu, are rare over the Arabian Sea, and most of the storms in this area tend to be small and dissipate quickly. The Indian monsoon flow was disrupted due to the formation of the super cyclone Gonu. A widespread area of convection was observed over the southeastern Arabian Sea on May 27, 2007 and then an organized tropical disturbance with a mid-level circulation developed at about 645 km off the coast, south of Mumbai, India, on 31 May. Late on 1 June, the system was classified as a depression by India Meteorological Department (IMD), and then it reached to a severe cyclonic storm by 3 June. The cyclone reached the maximum wind speed of 268 km h−1 (~145 knots) and the minimum sea-level pressure of 914 hPa by 1200 UTC 4 June; IMD classified the cyclone as super cyclone Gonu at this stage. After 6 h (i.e., 1800 UTC 4 June), the sea-level pressure deepened to 898 hPa. By 5 June, the cyclone weakened to a severe cyclonic storm. The eye of the cyclone became cloudy; the cyclone gradually weakened due to cooler sea-surface water temperature and dry air as it intruded the Arabian Peninsula. Because of interaction with land at Oman, the inner core of deep convection weakened rapidly, and the flow intensity decreased at a rate of 95 km h−1 (~26 m s−1). According to the IMD, cyclone Gonu crossed the eastern tip of Oman with the maximum wind of 150 km h−1 late on 5 June. After emerging into the Gulf of Oman, the cyclone re-intensified slightly. However, increasing wind shear and entrainment of the dry air from the Arabian Peninsula continued to suppress deep convection from its eastern semicircle. On 6 June, the cyclone turned to the north–northwest, and later the joint typhoon warning center (JTWC) downgraded Gonu to tropical storm category. Gonu crossed the Oman coast and subsequently the Makaran coast on 7 June. According to news reports, cyclone Gonu caused severe damages and a variety of losses throughout Oman and south Iran (worth $4 billion and $216 million US, respectively).
2.3 Experimental design
Summary of model experiments
Name of the experiment
Description of the experiment
Control run without data assimilation
GPS RO refractivity data from FORMOSAT-3/COSMIC
GPS + GTS
Combination of GPS and GTS
GPS cyclic assimilation of 12 h (with every 6 h + 6 h)
GTS cyclic assimilation of 12 h (with every 6 h + 6 h)
There are two kinds of refractivity operators in WRF 3DVAR to assimilate the GPS RO soundings, i.e., local and non-local refractivity operators. In brief, a local refractivity operator assimilates the RO retrieved refractivity as a local point measurement (Huang et al. 2005), whereas a non-local refractivity operator assimilates the integrated refractivity along a straight ray path (Liu et al. 2008; Ma et al. 2009). The non-local refractivity operator implemented in WRF 3DVAR (Chen et al. 2009) was used for this case study. The non-local refractivity operator is briefly described below.
3 The simulation results
3.1 Analyses of initial increments
3.2 Verification of the model results
The induced biases are larger for whole domain 1 due to high mountains of Tibetan plateau and the contrast between the land and the ocean. Therefore, we choose some part of domain 1 which is similar to domain 2, and the validation has been chosen to compare root mean square error (RMSE) of wind components (U and V) (m s−1), temperature (°C), mixing ratio (g kg−1), pressure (hPa) and refractivity (N-units). The RMSEs in general are similar when compared with ECMWF and NCEP analyses. Note that at the initial time, the difference between NCEP and CTL is identical to zero since the initial condition of CTL uses the NCEP global analysis. The difference following the time between ECMWF and GPS is larger than that between NCEP and GPS, because the initial condition of GPS also uses NCEP global analysis except with assimilation of the GPS RO data. The errors of U-component (Fig. 6a) are slightly larger for GPS as compared to CTL in the entire simulation period. The errors of V-component for GPS (Fig. 6b) are reduced from 48 to 96 h, so northerlies are more dominant to make the GPS track closer to the best track at the later stage of simulation. Moisture errors (Fig. 6d) for GPS are larger up to 66 h but become smaller from 72 to 96 h. The temperature errors are smaller for GPS at the later stage. The pressure errors (Fig. 6e) against both NCEP and ECMWF analyses for GPS are larger till 24 h but become smaller from 42 h to the end of simulation as compared to CTL. The refractivity errors (Fig. 6f) are reduced as well for GPS at the later stage, in consistency with moisture errors.
3.3 Cyclone track and intensity prediction
The assimilation of GTS and the combination of these two data sets show comparable tracks from 24 to 60 h, but later all these tracks deviate westward from the best track. All the assimilation experiments show better agreement with the observations on days 2 and 3 compared to day 4, but only GPS produces the track in best agreement with the best track on day 4. Compared to CTL, the track error for GPS is slightly larger on day 1, similar for day 2, but significantly reduced on days 3 and 4. Compared to GTS, the combination of GTS and GPS has shown slightly better performance in track prediction, which infers the benefit of GPS RO soundings. The cyclic run GTS-cyc does not improve the track prediction. For GTS data impact, the hot start seems to incur larger model errors than cold start at the CTL initial time. Nevertheless, the positive impacts of GPS RO data have been presented in both hot-start and cold-start runs.
The simulated minimum SLP at the cyclone center for every 6 h is shown in Fig. 8b. The observed minimum SLP at the model initial time is 978 hPa (0000 UTC 3 June) and then drops drastically until 30 h (0600 UTC 4 June). Later, the SLP slightly increases for 6 h but then deepens to 898 hPa at 1800 UTC 4 June, and then increases gradually to 982 hPa at 0000 UTC 7 June. None of the SLPs for the assimilation runs deepens to the observed. All the assimilation runs show the storm intensity of about 998 hPa at the model initial time, which is about 20 hPa higher than the observed SLP. This is because there is no bogus vortex inserted in the model initial conditions. We avoid vortex bogussing in this study to simplify the problem of demonstrating observation impact. GPS-cyc remarkably improves the storm intensity within 60 h compared to all other simulations. Compared to CTL, GPS also slightly improves storm intensity till 60 h. However, GPS + GTS is close to GTS till the end of integration. GTS-cyc catches weakening of the storm in day 4.
3.4 Possible mechanisms of predicted tracks
4 Forecast sensitivity to GPS RO data
GPS Sensitivity experiments
Name of the experiment
Description of the experiment
GPS sensitivity test 1
Removed RO soundings 1, 2, 5 and 7
GPS sensitivity test 2
Removed RO soundings 1, 2, 4, 5 and 7
GPS sensitivity test 3
Removed RO sounding 4
GPS sensitivity test 4
Removed RO sounding 1
GPS sensitivity test 5
Removed RO sounding 2
GPS sensitivity test 6
Removed RO sounding 5
GPS sensitivity test 7
Removed RO sounding 7
GPS sensitivity test 8
Removed RO sounding 3
GPS sensitivity test 9
Removed RO sounding 6
GPS sensitivity experiments for various heights
Name of the experiment
Description of the experiment
GPS above 3 km
GPS RO soundings retained above 3 km
GPS above 5 km
GPS RO soundings retained above 5 km
GPS above 8 km
GPS RO soundings retained above 8 km
GPS above 10 km
GPS RO soundings retained above 10 km
GPS above 12 km
GPS RO soundings retained above 12 km
GPS above 14 km
GPS RO soundings retained above 14 km
In this study, we employ the WRF version 2.2 model to simulate the super cyclone Gonu (2007) over the northern Indian Ocean. To improve the model initial conditions, the three-dimensional variational (3DVAR) data assimilation system has been used to assimilate different data sets including GPS RO refractivity soundings, GTS and their combination. A non-local refractivity operator (Chen et al. 2009) is applied to assimilate the GPS RO refractivity soundings available from FORMOSAT-3/COSMIC. Based on the model results, GPS RO refractivity soundings exhibit a remarkable positive impact on Gonu’s track prediction and outperform the others in terms of the cross-track error at later stages. The impacts have been investigated in detail in this study with regard to contributions from specific RO soundings and observational information above various heights.
Most of the initial moisture increments from the GPS RO soundings laid in a horizontal range of 500–700 km in the lower troposphere, exhibiting drier conditions near the center of the cyclone in the lower troposphere. Assimilation of GPS RO soundings produces dryness at lower levels and cooling in the upper troposphere. The GPS sensitivity experiments that assimilate only some of the GPS RO soundings show that the simulated tracks are more sensitive to the RO soundings in the vicinity of the cyclone. The simulated track is not improved when only the RO information above 14 km is retained. These experiments highlight the importance of upper tropospheric RO information in driving the cyclone. The GPS RO retrieved dry temperature is known to possess high accuracy in upper troposphere and lower stratosphere (UTLS) (Anthes et al. 2008). In this study, we found the positive impact of the GPS data on cyclone track prediction due to contributions from assimilation of their accurate temperature in the upper troposphere. It has been well known that the GPS RO retrieved refractivity may contain negative biases in the lower troposphere (Kuo et al. 2004; Poli et al. 2010; Anthes 2011). Therefore, the low-level information of RO data may be discarded while keeping their upper-level information, when biases are unavoidable in the retrieved refractivity at lower levels due to super-refraction conditions (e.g., Kuo et al. 2004; Sokolovskiy et al. 2003).
Further, we also compare the results of GPS assimilation experiment with GTS data assimilation. The relative advantages of the data impacts are discussed in this study. Overall, GPS performs best in terms of track prediction with a reduced cross-track error at later stages. To understand the track improvement at later stages (72–96 h) for GPS, we investigated the difference of moisture and sea-level pressure between GPS and CTL. It is noticed that a couplet was induced with low pressure and moist air to the left and high pressure and dry air to the right. This couplet pattern became stronger from 48 to 60 h. The horizontal pressure gradient force might be the reason for why the GPS track is driven closer to the best track at the later stage. For cyclone intensity prediction, GPS-cyc experiment outperforms the others due to use of more soundings. Owing to the high vertical resolution of RO soundings and high accuracy in retrieved temperature in UTLS, the RO soundings may help to better resolve the synoptic steering flow condition where the storm is embedded.
The impacts of GPS RO data in tropical cyclone prediction emerge through complicated nonlinear processes and are only partially illustrated or explained by initial and forecast difference analyses of specifically designed sensitivity experiments. We realize that any small tiny difference may likely result in significant forecast errors when tropical cyclone evolution is sensitive to some observation impact region. Furthermore, nonlinear advective and diffusive processes can mix up with microphysical processes, and in a long run, positive or negative impacts depend on how the cyclone is in response to the evolving processes. Thus, the combination of different sets of observations may not necessarily give further positive impacts even any of which does have a positive or neutral impact (Huang et al. 2010). In this study, we identify the mechanism that why the track can be driven dynamically due to positive impacts of observations.
In a prospective view, the impact of the GPS RO soundings on track prediction of a super cyclone, as shown in this study, encourages application of RO data to severe weather prediction. Optimistically, we may expect that more RO data in higher horizontal density should lead to larger impacts on cyclone prediction. The FORMOSAT-3/COSMIC follow-on mission (COSMIC-2) will start with a first launch of 6 LEOs in early 2016, followed by the second launch of 6 LEOs in 2018, to track signals from GPS and GALILEO and GLONASS (a Tri-G constellation network), and will provide daily RO data about four times the average amount (~2,000) from the FORMOSAT-3/COSMIC constellation. We are looking forward to exploring forecast impacts of plentiful RO data from such a future mission.
This work is supported by National Science Council and National Space Organization in Taiwan.
- Kliore AJ, Hamilton TW, Cain DL (1964) Determination of some physical properties of the atmosphere of Mars from changes in the Doppler signal of a spacecraft on an earth occultation trajectory. Jet Propulsion Laboratory, Technical Report, Pasadena, CA, pp 32–674Google Scholar
- Kuo Y-H, Sokolovskiy S, Anthes RA, Vandenberghe F (2000) Assimilation of GPS radio occultation data for numerical weather prediction. Terr Atmos Ocean Sci 11:157–186Google Scholar
- Kursinski ER, Hajj GA, Leroy SS, Herman B (2000) The GPS radio occultation technique. Terr Atmos Ocean Sci 11:53–114Google Scholar