The radar-enhanced GSI (version 3.1) system and the WRF-ARW (version 3.4.1) model were modified to assimilate radar/lightning-proxy reflectivity. First, cloud-to-ground lightning data were converted to reflectivity using a simple assumed relationship between flash density and reflectivity. Next, the reflectivity was used in the cloud analysis of GSI to adjust the cloud/hydrometeors and moisture. Additionally, the radar/lightning-proxy reflectivity was simultaneously converted to a 3D temperature tendency. Finally, the model-calculated temperature tendencies from the explicit microphysics scheme, as well as cumulus parameterization at 3D grid points at which the radar temperature tendency is available, were updated in a forward full-physics step of diabatic digital filter initialization in the WRF-ARW. The WRF-GSI system was tested using a mesoscale convective system that occurred on June 5, 2009, and by assimilating Doppler radar and lightning data, respectively. The forecasted reflectivity with assimilation corresponded more closely to the observed reflectivity than that of the parallel experiment without assimilation, particularly during the first 6 h. After assimilation, the short-range precipitation prediction improved, although the precipitation intensity was stronger than the observed one. In addition, the improvements obtained by assimilating lightning data were worse than those from assimilating radar reflectivity over the first 3 h but improved thereafter.
The development of nonhydrostatic numerical weather prediction (NWP) models with high resolutions, together with computational advances, has made refined weather forecasting possible. At present, however, sounding and satellite data cannot provide sufficient mesoscale or small-scale information to generate the initial field of the NWP model. The high temporal and spatial resolution of Doppler radar data provide not only the location and intensity of precipitation, but also the movement of hydrometeors, which are essential inputs to the initial field in a mesoscale NWP model. Nevertheless, ground-based Doppler radar has three main disadvantages. First, the detection range is limited to the scanning radius. Second, as most detection is limited to land, there are few observations of convective systems at sea. Finally, detection can be easily affected by terrain shadowing in mountainous regions. These disadvantages cause some difficulties when using radar data for the initialization of a refined forecast model. Recently, the rapid development of the lightning location network has provided a way of overcoming these drawbacks. Lightning is an indicator of deep convection, and lightning location network data can provide detailed information on convective clouds, such as the time, location, flash number, polarity, and intensity of discharge events. Furthermore, lightning data are relatively unaffected by geographical constraints and exhibit a higher temporal and spatial resolution than meteorological radar observations. Thus, lightning location network data can play an important role in convective weather research by supplementing radar data in the initialization of refined weather forecasting. Using both radar and lightning data would allow their complementary advantages to improve the accuracy of the representation of convective systems in the initial field of a model and could thus play an important role in increasing the accuracy of short-range convective weather forecasting. However, such an approach has not been widely studied.
Worldwide, there have been many studies of the methods that can be used to assimilate Doppler radar reflectivity. The principal methods used are the variational method [
Papadopoulos et al. [
The RUC/RR procedure has been used to assimilate lightning-proxy reflectivity transformed from lightning data based on the GSI cloud analysis system and appears to work very successfully. However, in the GSI (version 3.1) system released by the DTC (Development Testbed Center) and, in WRF-ARW (version 3.4.1), the function that assimilates radar/lightning-proxy reflectivity is not activated. In this paper, the GSI system and WRF-ARW are modified to assimilate radar/lightning-proxy reflectivity as in RUC/RR. We compare the assimilation of cloud-to-ground lightning data and Doppler radar data from Hefei in Anhui Province using the WRF-GSI system to examine the effect of the revised system.
The remainder of this paper is organized as follows. Section
In the GSI radar-enhanced cloud analysis system, 3D-grid cloud cover and precipitation types are set up using GOES-NESDIS (GOESTATIONARY-The National Environmental Satellite, Data, and Information Service) satellite cloud top products, surface METAR (meteorological terminal aviation routine) observations, lightning data, and radar reflectivity. Then, cloud and hydrometeor fields are updated in the cloud cover region. Additionally, 3D latent-heating based temperature tendencies are calculated from radar/lightning-proxy reflectivity, and convection areas are identified where the depth of the cloud cover is greater than 300 hPa. These two fields, including the derived latent-heating based temperature tendency and the “no-echo” array, are passed to the WRF-ARW model and are digitally filtered before the forecast is run. A flow chart for this procedure is shown in Figure
Flow chart of the radar-enhanced WRF-GSI system.
Most of the retrieving calculations and adjustments within the cloud analysis procedure are applied in the cloud cover region. In the GSI system, this cloud cover information is represented by a 3D volumetric cloud fraction (VCF). Therefore, accurate determination of the VCF is extremely important. The GSI system uses the background field (the WRF forecast), together with surface METAR (weather, clouds, ceiling, and visibility) data, GOES single field cloud top products provided by NESDIS (pressure, temperature, and cloud fraction), and 3D radar reflectivity mosaic and lightning data to generate the 3D VCF, cloud type, and precipitation type information.
In the radar/lightning-proxy reflectivity data process, if none of the above indicates that cloud is present, then the height of the cloud base is set to the lifting condensation level. Otherwise, the cloud base is set to the lowest level where the cloud fraction is larger than 0.2. For layers above the cloud base and with grid reflectivity larger than a certain threshold (>15 dBZ, below 2000 m; >10 dBZ, above 2000 m), the VCF is set to 1. Radar/lightning-proxy reflectivity provides the mid-troposphere information missed by surface and satellite observations, as well as giving a more accurate location for the cloud cover.
After constructing the 3D VCF, the latent-heating based temperature tendency field,
Areas of convection can only be identified if the vertical depth of the radar coverage is larger than 300 hPa. The maximum value of the temperature tendency in a column has to exceed 0.0002 K/s for the area to be considered a convection area. Otherwise, a no-convection area is determined and the CAPE (convective available potential energy) depth in the convection scheme is set to a near-zero value, effectively suppressing convection during DFI forward integration. Where the vertical depth of radar coverage is insufficient, the area remains unclassified.
Saturation water and ice vapor pressure are calculated using (
Before retrieving hydrometeors from the radar/lightning-proxy reflectivity, precipitation types were first classified using the grid radar/lightning-proxy reflectivity (GREF) and the wet-bulb temperature
Additional changes were made to the WRF-DFI model. The 3D latent heat calculated from the radar reflectivity and lightning data during the GSI process is averaged and then read in during the DFI forward integration to replace the model latent heating rate derived from both explicit and parameterized precipitation schemes. This replacement may lead to dynamic and thermal responses consistent with convection-associated latent heating. Furthermore, convection is inhibited over the “no-echo” area where radar/lightning-proxy reflectivity shows an absence of convection. In the official WRF-DFI model, all variables, including the wind fields, temperature, pressure, and moisture fields, undergo digital filtering in the DDFI forward and backward integration. However, in the modified framework used here, the hydrometeors (cloud liquid water/ice, rain, snow, and hail), but not the vapor mixing ratio, were all set to the initial value at the end of the backward (integrating the adiabatic, reversible terms in the equations only) and forward (using full physics and mixing/dissipation terms) integration.
Lightning, strong wind, hail, and rainstorms occurred in most regions of Anhui Province from the afternoon until midnight on June 5, 2009. This convective weather system led to serious economic losses and casualties. The Department of Civil Affairs reported that 4.82 million people were affected by the storm, with 25 people killed, 3 people lost, and 215 people injured; the total area of crops destroyed was 205.4 km2. This convective weather system influenced a broad region that included most of Anhui Province, the western part of Jiangsu Province, and the Jiujiang area of Jiangxi Province. The development of this convective weather system is shown in Figure
Comparison of (a) cloud-to-ground lightning strikes counted on a grid from 0530 to 0610 UTC, (b) lightning-proxy composite reflectivity (dBZ), and (c) observed composite reflectivity (dBZ) at 0600 UTC.
The Doppler radar at Hefei (117.716°E, 31.883°N) in China is a 10 cm wavelength Doppler radar with a 1° half-power beam width. The data consist of volume scans of radar reflectivity, radial velocity, and spectrum width collected in volume scan mode during the period of precipitation, with the elevation angle increasing in steps from 0.5° to 19.5°. The number of elevation angle steps and the temporal resolution of the data depend on the operational mode of the radar. Accordingly, reflectivity values are recorded at 1 km intervals along the radar beam, whereas velocity parameters are recorded at 250 m intervals. Each volume scan takes approximately five minutes. The lightning flash data used in this study were obtained from the lightning location network of Anhui Province, which consists of 11 lightning location systems in Fuyang, Chuzhou, Liuan, Anqing, Huangshan, Xuancheng, Bengbu, Huaibei, Tongling, Haozhou, and Hefei. This network is primarily used to detect cloud-to-ground flashes and can differentiate the polarity of flashes.
The lightning flash data were transformed into 3D proxy radar reflectivity based on a simple assumed relationship between flash density within a given RUC grid (13.545087 km) and the corresponding column maximum of grid-averaged reflectivity in the GSI code:
The correlation coefficient between proxy reflectivity and observed radar reflectivity from 0600 to 0900 UTC was 0.847, and the root mean square error (RMSE) was 6.062. Figure
To study the effect of lightning data on the forecast of this convective weather system using the radar-enhanced WRF-GSI system, two assimilation experiments were performed. Both experiments used NCEP (National Centers for Environmental Prediction) FNL (Final) Operational Global Analysis as background and lateral boundary conditions, and the initial forecasts started at 0000 UTC, June 5, 2009, and ran for 6 h. Exp. lghtn assimilated lightning data from Anhui Province at hourly intervals between 0600 and 0900 UTC, followed by a 6 h WRF model forecast. Exp. radar assimilated the Hefei radar reflectivity data at hourly intervals from 0600 to 0900 UTC, followed by a 6 h WRF model forecast. In this study, the complex cloud analysis package in the radar-enhanced GSI system was used to assimilate radar/lightning-proxy reflectivity. All these forecasts were compared with a baseline control forecast (Exp. CTL) that started directly from the NCEP FNL analysis at 0000 UTC.
All experiments were performed on a domain centered at the Hefei radar, with a horizontal spacing of 13.545 km. The model network was composed of 101 × 101 × 50 grid points. The pressure at the top of the model was set to 10 hPa, and the time step for integration was 60 s. The main model physics included the Lin et al. scheme [
To analyze the effect of assimilating lightning data in this mesoscale convective system case, we focused on Exp. lghtn from the radar-enhanced WRF-GSI system. Figure
Analyzed maximum reflectivity (dBz) in (a) Exp. CTL, (b) Exp. lghtn, and (c) Exp. radar at 0600 UTC.
The first column of Figure
The distributions of horizontal and vertical increments for experiments lghtn and radar at 0600 UTC. The first two columns are horizontal increments and the last two columns are vertical increments along 32.89°N. (a1) is the lightning-proxy reflectivity at the 18th level and (a2) is the observed radar reflectivity at the 18th level (unit: dBZ). (a3) is the vertical distribution of lightning-proxy reflectivity, and (a4) is the observed radar reflectivity. Rows two to seven are the increments of perturbation potential temperature (unit: K), water vapor mixing ratio, rain water mixing ratio, snow mixing ratio, ice mixing ratio, and cloud water mixing ratio, respectively (unit: g/kg).
The last two columns of Figure
The ETS of the forecast composite reflectivity for the five experiments at (a) 10, (b) 15, (c) 20, (d) 25, and (e) 30 dBZ.
Hourly composite reflectivity from 0900 to 1500 UTC. Column 1 shows the observed radar composite reflectivity (dBZ), and columns 2–4 are the forecast composite reflectivity of Exp. CTL, Exp. lghtn, and Exp. radar, respectively.
To compare the effect of assimilating lightning data at different times on the forecasts, Exp. lghtn was divided into four separate experiments: da_1, da_2, da_3, and da_4. Experiment da_1 assimilated lightning data at 0600 UTC using the forecast field from Exp. CTL at 0600 UTC as a first guess field. Experiment da_2 also assimilated lightning data at 0700 UTC based on Exp. da_1. Similarly, da_3 and da_4 also assimilated lightning data at 0800 UTC and at 0900 UTC, respectively. Equitable threat scores (ETS) [
The behavior of the forecast composite reflectivity can be seen more clearly in Figure
After a few hours, this overestimation was less clear, although the location of the forecast region of composite reflectivity moved away from its observed location. Nevertheless, the maintained forecast composite reflectivity was perfect after assimilation and could last for more than 6 h, which indicated that the weakening of reflectivity was not clear due to a correct coordination between variables in the radar-enhanced WRF-GSI system after assimilation.
Figure
The ETS of the forecast composite reflectivity for Exp. CTL, Exp. lghtn, and Exp. radar at (a) 10, (b) 15, (c) 20, (d) 25, (e) 30, and (f) 35 dBZ.
The forecast precipitation (mm) for the three experiments over different time intervals. Column 1 shows the observed precipitation, and columns 2–4 the forecast precipitation from Exp. CTL, Exp. lghtn, and Exp. radar, respectively. The time interval of row 1 is 0900–1200 UTC, and the time intervals of rows 2 and 3 are 1200–1500 UTC and 0900–1500 UTC, respectively.
A better understanding of the effect on precipitation can be obtained by comparing the observed precipitation with the forecast 3 and 6 h precipitation from the three experiments (Figure
Figure
The threat scores of the three experiments for different precipitation thresholds: (a) 1 mm and (b) 10 mm.
Lightning location data are an indicator of convective weather and can provide detailed information such as time, location, number, polarity, and intensity of discharge events that relate to convective clouds. Furthermore, lightning data are relatively unaffected by geographical constraints and exhibit higher temporal and spatial resolution than meteorological radar observations. Lightning location network data can thus play an important role in convective weather research and in particular can be a useful supplement to radar data in the initialization of refined weather forecasting. In the radar-enhanced WRF-GSI system, lightning location network data were first converted into 3D proxy radar reflectivity assuming a simple relationship between flash density and reflectivity. Then, the reflectivity information was used in a complex cloud analysis in the GSI system to improve the cloud/hydrometeor and moisture distributions. Additionally, the radar/lightning-proxy reflectivity was also simultaneously converted to a 3D temperature tendency field. Finally, the model-calculated temperature tendencies from the explicit microphysics scheme and the cumulus parameterization at 3D grid points where the radar temperature tendency was available were updated in the forward full-physics step of diabatic digital filter initialization (DDFI) in the WRF-ARW3.4.1 core. In this study, three experiments were designed to test the system using an MCS case that occurred on June 5, 2009, assimilating Hefei Doppler radar and lightning data from Anhui Province. The conclusions of this study can be summarized as follows. There was a high correlation between the converted lightning-proxy reflectivity and Hefei Doppler radar observed reflectivity, with a correlation coefficient of 0.847 and a root mean square error of 6.062. After cycled assimilation, the forecast composite reflectivity matched the observed reflectivity better than the parallel experiment without assimilation, and the maintained forecast composite reflectivity was perfect and could last for more than 6 h, due to the correct relationship between variables in the radar-enhanced WRF-GSI system. Assimilating lightning location network data in the enhanced WRF-GSI system can achieve a better improvement in 3 and 6 h precipitation forecasts than assimilating radar reflectivity data, although there is a shift in the spatial location and an overestimation of the precipitation intensity. Assimilating lightning location network data can give a better result because the area and intensity of lightning-proxy reflectivity were smaller than those of the observed radar reflectivity. Hence, the increments of perturbation potential temperature and of water vapor mixing ratio must be properly decreased when assimilating observed radar reflectivity and lightning-proxy reflectivity. Based on the above conclusions, the revised radar-enhanced WRF-GSI system exhibited an excellent capacity for refined convective weather forecasting in a mesoscale NWP model after assimilating lightning location network data. However, there was an overestimation of the intensity of precipitation and a deviation in the spatial location in the first few hours of forecasting, which needs to be studied further so that the assimilation method can be revised and improved in the future.
The authors declare that there is no conflict of interests regarding the publication of this paper.
This research was supported by the National Key Basic Research Program of China (2014CB441406 and 2013CB430102) and the National Science Foundation of China (no. 41175092). The authors wish to thank the Weather Service Forecast Office of Anhui Province for providing radar and lightning data.