Reprint 429

Impact of Intense Observations on the Model Analysis and

Prediction of 9 June Rainstorm in 1998

C.C. Lam, W.K. Wong & S.T. Lai

Scientific Conference on

the South China Sea Monsoon Experiment (SCSMEX),

Shanghai, China, 17-20 April 2001

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Impact of Intense Observations on the Model Analysis andPrediction of 9 June Rainstorm in 1998

Queenie Ching-Chi LAM1, Wai-Kin WONG and Edwin Sau-Tak LAI

Hong Kong Observatory134 A Nathan Road, Tsimshatsui, Hong Kong, China

Abstract

In routine operations, observational data retrieved via the GlobalTelecommunication System are mainly of synoptic-scale resolution. Most of theupper-air sounding data are available every 12 hours. These sparsely distributeddata will not be able to capture detailed features in mesoscale weather systems andmay cause spin-up problem in model rainfall forecast. The model state cannot beadjusted by observations and may drift slowly away from the actual through forecast-forecast cycle rather than analysis-forecast cycle, especially when the cycle is updatedfrequently.

Operationally, moisture initial field is improved by assimilating Doppler radarreflectivity data within 250-km range from Hong Kong and through the use ofphysical initialization. The radar reflectivity data are calibrated against local surfaceraingauge data in hourly composite rainfall analyses. Enhanced surface raingaugedata over the south China coastal region are utilized to calibrate radar data in thesurrounding area of Hong Kong, thus producing a more accurate rainfall analysis formoisture and diabatic initialization in the model.

In this paper, the impact of enhanced surface and upper-air data during theIntensive Observing Period of the South China Sea Monsoon Experiment onforecasting the rainstorm on 9 June 1998 will be investigated using the OperationalRegional Spectral Model. The effect of model resolutions in simulating therainstorms with and without the use of enhanced observations will be explored. Thesensitivity of enhanced observations and model resolutions will be analyzed,especially in terms of rainfall forecast.

1. Introduction

Heavy rainstorms affected the south China coastal region on 9 June 1998. Theybrought more than 400 millimeters of rain to Hong Kong and broke the maximumdaily rainfall record for June at the Hong Kong Observatory (HKO) since 1884. Thehourly rainfall rate was as high as 100 millimeters per hour.

The rainstorms occurred within the Intensive Observing Period of the SouthChina Sea Monsoon Experiment (SCSMEX). Enhanced surface data from hourlySYNOP reports, Automatic Weather Stations (AWS) and ships as well as enhanced

1 Queenie Ching-Chi LAM, tel: (852) 2926 8452, fax: (852) 2311 9448, email: nwp@hko.gcn.gov.hk

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upper air data from radiosondes in 6-hourly full ascents with extra levels of 975, 950and 900 hPa were available for the study of the rainstorm event. The impact ofenhanced observations on forecasting the rainstorm event in short-range will beinvestigated through numerical simulation studies using HKO's Operational RegionalSpectral Model (ORSM).

The synoptic and mesoscale background for the rainstorm events will be outlinedin Section 2. Details of the ORSM are described in Section 3 while the simulationresults and discussions are given in Section 4. Finally, conclusions are summarizedin Section 5.

2. Background of the 9 June rainstorm

A trough of low pressure lingered over the coast of western Guangdong while anarea of high pressure was centered near 24° N, 117 ° E on 8 June 1998. The east tonortheasterly surface winds originated from the high pressure cell prevailed over thecoast of eastern Guangdong. They converged with the south to southeasterliesoriginated from the seas in the vicinity of Hong Kong. The surface convergence wasjust off the coast near Shanwei (marked by arrows in Fig. 1(a)). A moisture ladensouthwesterly jet passed to the south of the trough at 850 hPa near 25° N. Thissupplied ambient moisture to the south China coastal region for the development ofconvection. The lower troposphere over the region was very moist and unstable.Meso-α scale vortices with diameter of around 300 km could be analyzed on the 850-hPa wind field near the tail-end of the low level trough over Guangxi (marked A inFig. 1(b)). There were organized convection associated with these vortices as seenfrom the IR satellite imageries (marked A' in Fig. 2(a)). The southwesterlies overthe coast of Guangdong were enhanced with the approach of the mesoscale vorticesand winds picked up to around 30 knots on the morning of 9 June. There were alsoactive low latitude short waves propagating along the south China coast at the time.

The eastward-moving 500-hPa trough near 30° N, 110 ° E brought about thesouthward advancement of the low level trough towards the south China coast.Figure 1(c) shows the mean sea-level pressure (MSLP) analyzed at 00 UTC 9 June.The intrusion of cooler air from the north converged with the warm and moisture-laden southwesterlies and triggered the generation of mesoscale cyclonic shear in thelow levels. By that time, a significant directional outflow at 200 hPa was found nearthe coast of western Guangdong (marked by arrows in Fig. 1(d)). The synoptic setupwas generally favorable for convective development.

Fig. 3(a)-(f) show the three-hour accumulated rainfall maps derived from radarreflectivity and surface raingauge data for the episode of heavy rain affecting HongKong on 9 June. The quasi-stationary radar echoes (marked X in Fig. 3(a)) wereassociated with surface convergence off the coast of Shanwei to the east of HongKong. Convection in meso-β scale developed just to the west of the Pearl RiverEstuary in the small hours of 9 June. They intensified as they crossed the estuarytowards Hong Kong (marked X' in Fig. 3(a)). Radar image shows that the rainechoes intensified further as they approached the pre-existing quasi-stationary echoesjust to the east of Hong Kong (X+X' in Fig.3(c)). This brought the first lot of heavy

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rain to Hong Kong in the early morning of 9 June. During the day, there werefurther outbreaks of heavy rain in Hong Kong as a result of continuous developmentof rain clouds upstream near the coast of western Guangdong. Such convectiveactivity was sustained by more eastward-moving mesoscale perturbations triggered bythe proximity of the surface trough and 850-hPa vortices.

3. Model description

The ORSM was formulated in hydrostatic primitive equations for momentum,mass, specific humidity and virtual temperature in advective form. Details of modelformulation are given in NPD/JMA (1997).

In model analysis, surface pressure, geopotential height, wind, temperature andrelative humidity fields were analyzed using first guess from the previous 6-hourmodel run. Observational data including SYNOP, AWS, SHIP, BUOY, TEMP,PILOT, AIREP, AMDAR, SATEM, SATOB and ATOVS etc., as well as six-hourlydigital cloud amount data from the Geostationary Meteorological Satellite (GMS) ofJapan Meteorological Agency (JMA) were decoded and assimilated with qualitycontrol. These data were objectively analyzed using multi-variate three-dimensionaloptimal interpolation (OI) on model levels. The data cut-off time was one hour.

Operationally, the model was configured to run at 20-km resolution in the innerdomain inside a 60-km resolution outer model through one-way nesting. The 60-kmmodel provides boundary conditions for the 20-km model. In this study, ORSM wasalso configured to run at 10-km resolution and the boundary conditions were preparedfrom the 20-km model. Table 1 shows a summary for the model configuration.

In the model experiments, the effect of physical initialization using rainfallanalysis data was also tested. Hourly rainfall analysis data, ingested into the modelthrough a 3-hour pre-run, were used to adjust the thermodynamic fields liketemperature, moisture and heating profile. The divergent field corresponding to therainfall analysis was also initialized through diabatic non-linear normal modeinitialization (Matsumura et al., 1995, Takano et al., 1988).

Hourly rainfall analysis data were derived from the radar reflectivity data within250-km range of Hong Kong and calibrated by surface raingauge data. Instead ofmerely using the conventional Marshall-Palmer formula, rainfall rate was estimatedby minimization of error in a least square sense to find an optimal reflectivity -rainfall (Z-R) relationship. The radar data coverage represented only a portion of themodel domain. Digital cloud information derived from satellite data was used toestimate the rainfall rate outside the radar range. However, as the satellite digitalcloud data were only available at 00, 06, 12, and 18 UTC, there were cases for theasynoptic hours that only a limited portion of the model domain underwent physicalinitialization process during the model pre-run period. Hourly rainfall analysis datawere inserted three times for three consecutive hours before the analysis hour in the20-km model.

The 60-km model was cold-started at 06 UTC 8 June 1998 with the first-guessfield taken from JMA's Global Spectral Model (GSM) analysis. 6-hour analysis-

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forecast cycle run was carried out for the 60-km model which provided boundary datato the 20-km model at hourly interval. The 20-km model cycle run was performed at3-hourly intervals to provide 24 hours forecasts.

4. Model results

Model experiments were carried out using different combinations of data typesand model configurations denoted by the following nomenclature:

IOP - all available intensive observationsnoIOP - no intensive observationsGD-AWS - noIOP plus enhanced Guangdong automatic weather station dataTEMP - noIOP plus enhanced radiosondes dataPI - physical initializationnoPI - no physical initialization

The impact of enhanced observations and various data types among IOP, noIOP,GD-AWS and TEMP, model settings between PI and noPI, as well as different modelresolutions and initial times were studied. The forecast range was confined to thefirst 24 hours. Model resolutions of 60 km, 20 km and 10 km were used. Theeffect of physical initialization was tested in the 20-km model experiments. PI wasnot applied in the experiments unless otherwise stated.

Model performance was verified against ECMWF 00, 06, 12, 18 UTC analysisfields at 0.5 deg resolution. Differences in MSLP, vector wind and relative humidityfields for the area 15-30° N, 105-125 ° E were calculated for results comparisonbetween different model experiments. Assessment of rainfall forecasts was based on3-hourly accumulated rainfall. 3-hourly rainfall analysis fields, derived fromDoppler radar reflectivity within 250-km range of Hong Kong and calibrated usinglocal as well as Guangdong surface raingauge data, were taken as ground truth for thepurpose of verification and comparison (Fig. 3).

4.1 IOP versus noIOP

The distributions of surface and upper air data in IOP and noIOP sets for 12 UTCand 18 UTC 8 June 1998 are shown in Fig. 4 and 5 respectively.

60-km model runs

In the model run initialized at 06 UTC 8 June 1998, significant differences werefound in the prediction of rain area and intensity over the south China coastal areasbetween IOP and noIOP in the first 12-hour forecasts (e.g. T+9 forecast of rainfallvalid at 15 UTC on 8 June 1998 in Fig. 6). Rain development in the noIOP run wasvery much under-estimated. For the MSLP fields, pressure over western Guangdongwas about 1-2 hPa too high in the initial analyses and up to T+12 forecasts (Fig. 7).The differences became smaller from T+12 to T+24 forecasts (Fig. 8).

At model initial times of 12 UTC and 18 UTC 8 June, differences in the rainfalland MSLP fields between the IOP and noIOP runs were noticeable for the first few

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hours but becoming less significant beyond T+6 forecasts (e.g. T+9 forecast valid at21 UTC 8 June 1998 in Fig. 9).

In general, the dynamical fields were better initialized in the IOP run. It wasalso observed that enhanced observations in the IOP run had less impact on modelinitial and forecast fields at 12 UTC when differences in data availability were lessbetween the IOP and noIOP runs. There was also a trend of decreasing range ofinfluence in later IOP runs after the analysis-forecast cycle had kicked in.

20-km model runs

At model initial time of 12 UTC 8 June, the ridging feature over the coastal areasof eastern Guangdong was much better depicted in the IOP run (Fig. 10). The initialconvergence field was therefore closer to what was actually observed in the synopticanalyses (also see Fig. 1(a)). There were also notable differences in model rainfallforecast in the first few hours (Fig. 11). Heavy rain development along the southChina coast on the night of 8 June and the morning of 9 June in the IOP run showedstronger resemblance to the actual situation (see also Fig. 3). Verification ofmoisture fields at 850 and 700 hPa showed excessive moisture off the east coast ofHainan Island were adjusted in the model initial field in the IOP run.

Continuous rainstorm development over the coast of western Guangdong in the

T+12 to T+24 forecasts was accurately predicted in both the IOP and noIOP runs.However, in the latter, rainstorms were forecast to weaken as they moved east towardsHong Kong. In the IOP run, another episode of heavy rain with reduced intensitywhich affected Hong Kong in the afternoon of 9 June was also accurately captured.For the 18 UTC run in particular (Fig. 12), the pattern of IOP T+12 rainfall forecastcompared favorably with the satellite imagery (Fig. 2(b)).

In general, for noIOP runs at different initial times, there was a consistent trendof weakening rainstorms as they moved in from western Guangdong. Thesouthward advance of the trough of low pressure over southern China was also foundto be too fast in the later part of the forecast period.

4.2 GD-AWS versus noIOP

Fig. 13 shows GD-AWS T+3 rainfall forecast at the initial times of 12 UTC and18 UTC on 8 June. The pattern of T+24 rainfall forecast valid at 15 UTC 8 June wassimilar to the IOP result but very different from the noIOP run (Fig.13(a) comparedwith Fig. 11(a) and (b)). This might suggest that GD-AWS data had a significant roleto play in the successful prediction of rainstorms in the IOP model experimentsundertaken in this study. For the subsequent run initialized at 18 UTC, rainfallforecast in the region (Fig. 13(b)) was generally over-predicted compared with actualrainfall analysis for the same period.

4.3 TEMP versus noIOP

In contrast to the GD-AWS run, the impact generated by the TEMP 20-km run(Fig. 14) was less conclusive. In the first few hours of the model run initialized at 12UTC 8 June, the area of rain development was located further to the south over the

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northern part of the South China Sea, somewhat similar to the noIOP forecasttendency. However, towards the later part of the forecast period, the location of therain development area was adjusted towards the IOP scenario, albeit with reducedrainfall intensity.

4.4 PI versus noPI

At 12 UTC 8 June with IOP, heavy rain development just to the west of the PearlRiver Estuary in the first few hours was better forecast in the PI run (Fig. 15) than inthe noPI run (Fig. 11(a)). In the case of noIOP, application of PI would helpmaintain the intensity of rainstorms later on 9 June when the model initial times werecloser to the time of rainstorm event. In the absence of sufficient observations, PIwould be able to adjust the initial moisture fields so as to alleviate the spin-upproblem in rainfall forecast.

4.5 60-km versus 20-km versus 10-km model resolutions

In the 60-km model runs, the rainfall patterns appeared to have been smoothedout and the timings for the several episodes of heavy rain were not well resolved.Discrete rain areas about 100 km in diameter could be forecast by the 20-km ORSMand the signals were generally much sharper.

The effect of IOP was found to be more prominent in higher resolution runs,particularly noticeable in the first 6 hours. Fig. 16 shows T+3 rainfall forecast by10-km ORSM initialized at 12 UTC 8 June. The rainfall amount could be around 40% higher in the 10-km run compared with the 20-km run. However, the impact ofmodel resolutions was generally small in terms of forecast rainfall patterns.

5. Conclusions

Model experiments were performed with various data types and different modelsettings to study the impact of intensive observations on the model analysis andforecast of rainstorms on 9 June 1998. The emphasis was on short-range rainfallforecast up to 24 hours ahead.

Numerical simulation using ORSM with 60-km, 20-km and 10-km resolutionsdemonstrated that the use of intensive observations improved model initial fields andshort-range forecasts, particularly in the first few hours. The impact of IOPobservations was increasingly prominent in higher resolution runs. The increase ofmodel resolutions could produce higher rainfall amount in some cases. Finertemporal and spatial features could be depicted in higher resolution runs. However,differences in the overall forecast rainfall distribution patterns were generally small inthe ORSM simulations.

Impact was also more noticeable for model runs with initial times other than 12UTC when the number of observations differed significantly between the IOP andnoIOP runs. However, through the analysis-forecast cycle, the effect of IOP tendedto decrease in later runs.

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For IOP observations, the dense automatic weather station data over Guangdongseemed to be most crucial in terms of good forecast performance. Enhancedradiosonde data, limited in time and space, only had slight positive impact on themodel forecasts.

As model performance is related to the quantity as well as quality ofobservational data, the length of forecast cycle may also affect the simulation results.Extended numerical experiments and more rainstorm cases may be required for asystematic and objective assessment of the SCSMEX IOP data impact.

Acknowledgment

The authors wish to thank C.K. Chow, K.Y. Chan, C.H. Lam and Y.K. Sing fortheir assistance in data encoding and decoding as well as preparation of rainfallanalysis data. The ORSM was originally developed by JMA and the cloud imagerywas originally obtained from GMS of JMA.

References

Matsumura T, I. Takano, K. Aonashi and T. Nitta, 1995: Improvement of spin-up ofprecipitation calculation with use of observed rainfall in the initialization scheme. J.Meteorol. Soc. Jpn.,73, 353-368.

NPD/JMA, 1997: Outline of the operational numerical weather prediction at the JapanMeteorological Agency. Appendix to progress report on numerical weatherprediction, 48-63.

Takano, I., H. Nakamura and Y. Tatsumi, 1988: Nonlinear NMI for spectral limitedarea models, JMA/NPD Technical Reports No. 26.

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ORSMHorizontal resolution 60 km 20 km 10 kmNo. of vertical levels 36 36 36Model top (hPa) 10 10 10Map Projection Mercator Mercator MercatorHorizontal no. of gridpoints

151 x 145 151 x 145 151 x 145

Domain coverage 9° S-59° N, 65-152° E 9-35° N, 100-128° E 17-30° N, 106-120° EAnalysis time 00, 06, 12, 18 UTC 00, 03, 06, 09, 12, 15,

18, 21 UTC00, 03, 06, 09, 12, 15,

18, 21 UTCInitial condition 60-km ORSM analysis 20-km ORSM

analysis10-km ORSM analysis

Boundary condition JMA's GSM forecasts 60-km ORSMforecasts

20-km ORSM forecasts

Table 1. Model configurations of ORSM for the impact study.

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(a) (b)

(c) (d)

Fig.1 (a) MSLP analysis at 00 UTC 8 June 1998. Solid line indicates theposition of the trough of low pressure; (b) Streamline analysis of 850 hPa field at12 UTC 8 June 1998; (c) MSLP analysis at 00 UTC 9 June 1998; (d) Streamlineanalysis of 200 hPa field at 00 UTC 9 June 1998.

(a) (b)30 N

25 N

20 N

15 N105 E 110 E 115 E 120 E 125 E 105 E 110 E 115 E 120 E 125 E

Fig.2 IR satellite image for (a) 12 UTC 8 June 1998; (b) 06 UTC 9 June 1998.

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(a) (b)

(c) (d)

(e) (f)

Fig. 3 3-hour accumulated rainfall analysis ending at (a) 15 UTC; (b) 18 UTC;(c) 21 UTC 8 June 1998; (d) 00 UTC; (e) 03 UTC and (f) 06 UTC 9 June 1998.

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(a) (b)

(c) (d)

Fig.4 Distribution of surface data and radiosonde data respectively in (a) and (b)for IOP Set; (c) and (d) for noIOP Set. Valid time is 12 UTC 8 June 1998.

(a) (b)

(c) (d)

Fig.5 Same as Fig. 4 but the valid time is 18 UTC 8 June 1998.

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(a) (b)

Fig.6 60-km ORSM 9-hour forecast of 3-hourly accumulated rainfall, MSLPand surface wind fields in (a) IOP; (b) noIOP Sets valid at 15 UTC 8 June 1998.

(a) (b)

Fig.7 Mean error of 60-km ORSM MSLP analysis at 06 UTC 8 June 1998 for(a) IOP; (b) noIOP Sets.

(a) (b)

Fig.8 Mean error of 60-km ORSM 24-hour MSLP forecast initialized at 06UTC 8 June 1998 for (a) IOP; (b) noIOP Sets.

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(a) (b)

Fig.9 60-km ORSM 9-hour forecast of 3-hourly accumulated rainfall, MSLPand surface winds based on 12 UTC 8 June 1998 analysis for (a) IOP; (b) noIOPSets.

(a) (b)

Fig.10 20-km ORSM MSLP analysis for (a) IOP; (b) noIOP Sets.

(a) (b)

Fig.11 20-km ORSM 3-hour forecast of 3-hourly accumulated rainfall, MSLPand surface winds based on 12 UTC 8 June 1998 analysis for (a) IOP; (b) noIOPSets.

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(a) (b)

Fig.12 20-km ORSM 12-hour forecast of 3-hourly accumulated rainfall, MSLPand surface winds based on 18 UTC 8 June 1998 analysis for (a) IOP; (b) noIOPSets.

(a) (b)

Fig.13 20-km ORSM 3-hour forecast of 3-hourly accumulated rainfall, MSLPand surface winds based on (a) 12 UTC; (b) 18 UTC 8 June 1998 analysis forGD-AWS Set.

Fig.14 Same as Fig.13(a) but for TEMP Set.

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Fig.15 Same as Fig.11(a) but with PI.

Fig.16 Same as Fig.11(a) but using 10-km ORSM.