JOURNAL OF ELECTRONIC SCIENCE AND TECHNOLOGY, VOL. 17, NO. 2, JUNE 2019 161

Digital Object Identifier:10.11989/JEST.1674-862X.80730010

Infrared Radiance Simulation and Application under Cloudy Sky Conditions Based on HIRTM

Jian-Hua Qu | Jun-Jie Yan* | Mao-Nong Ran

Abstract—An algorithm based on hyperspectral infrared cloudy radiative transfer model (HIRTM) is introduced and a simulation method for infrared image of the generation geostationary meteorological satellite is proposed. Based on the parameters from weather research and forecast (WRF), such as the water content, atmospheric temperature, and humidity profile, the simulation data for the advanced Himawari imager (AHI) infrared radiative (IR) channels of Himawari-8 are obtained. Simulated results based on HIRTM agree well with the observed data. Further, the movement, development, and change of the cloud are well predicated. And the simulation of IR cloud image for the weather forecast has been obtained. This paper provides an improved method for evaluation and improvement of regional numerical model for weather forecast.

Index Terms—Hyperspectral infrared cloudy radiative transfer model (HIRTM), regional numerical model, satellite cloud image.

1. Introduction

The meteorological satellite data has attracted lots of attention of research institutes, companies, and governments for its unique characteristics and advantages[1]. High-resolution remote sensing data plays a key role in the fields of weather analysis and forecasting, climate change research[2], environmental monitoring[3], and disaster prevention and reduction[4],[5], which is critical for the economic development of a country. Recently, the weather forecast theory and the data acquisition technology have been improved effectively. Meanwhile, methods and technologies have been greatly improved for weather forecast. However, the catastrophic weather, such as rainstorm, is still hard to be predicted, and the meteorological departments are unable to present a timely warning for residents[6]. In addition, the rainstorm details (time, location, and intensity) and prediction timeliness still have great limitations. Therefore, making full use of the observed data, especially the high-resolution data of the new geostationary meteorological satellite, is one of the effective means to improve the early warning and the prediction of the disaster weather in the future. The new generation geostationary meteorological satellites has been launched by , United States, and Japan. These satellites carry advanced high-resolution observation instrument (1-minute to 15-minute observational frequency, 0.5 km to 4.0 km spatial resolution), which can be helpful to increase the forecast

*Corresponding author Manuscript received 2018-07-25; revised 2018-09-19. This work was supported by the Climate Change Special Project under Grant No. CCSF201834. J.-H. Qu, J.-J. Yan, and M.-N. Ran are with Huayun Shinetek Company, China Meteorological Administration, Beijing 100081, China (e-mail: [email protected]; [email protected]; [email protected]). Publishing editor: Xin Huang 162 JOURNAL OF ELECTRONIC SCIENCE AND TECHNOLOGY, VOL. 17, NO. 2, JUNE 2019 accuracy of weather and climate in China[7],[8]. Although the quantitative application of Fengyun-4 will be the main development direction, the traditional qualitative identification (satellite cloud image) is still a popular method for weather analysis and severe convective weather monitoring. With the high temporal and spatial resolution of the multi-spectral infrared brightness temperature measurement, the forecasters need to recognize current weather situation from the cloud map, and then predict the change of the mesoscale weather system in the next 1 day to 7 days according to the numerical weather forecast (NWP). Compared with the situation of NWP, simulated satellite cloud images can provide more information for forecasters. The simulated method based on the NWP can improve the effect of the observed data of the new geostationary meteorological satellite, which is significant for the daily weather forecast. Cloud simulation can convert the output of the NWP model into the simulated satellite cloud image, establishing an operator or mapping between the atmospheric state and the satellite observation for transforming the atmosphere parameters into satellite observed data. Since cloud has great influence on the simulation results of the radiative transfer model, more attention has been attributed to researches on cloud simulation under cloudy conditions. Two typical radiative transfer models, the general radiative model community radiative transfer model (CRTM)[9] from United States and the radiative transfer for the tiros operational vertical sounder (RTTOV)[10] developed by Europe, have been widely used. Although these two models consider the radiative effects of aquatic products and have been improved effectively, there are still lots of limitations in the simulation of the infrared band under the cloudy sky, especially in cloud radiance or brightness temperature calculation. Aiming to solve the problems of slow calculation speed and low precision, a fast and accurate radiative transfer model: Hyperspectral infrared cloudy radiative transfer model (HIRTM)[11],[12], for infrared bands is proposed to improve the simulated accuracy of brightness temperature in the cloudy sky based on RTTOV and CRTM. This research proposes a simulation system based on HIRTM by transferring weather research and forecast (WRF) data to the simulated data.

2. Algorithm and Processes

The radiative transfer model is an observation operator, which is the fundamental for the direct assimilation of satellite radiative data and cloud simulation. According to the atmospheric temperature, humidity profile, and surface state variables, the fast radiative transfer model follows the observation direction (scanning angle) of the satellite scanner and calculates the simulated observation value of the satellite in high precision by using the spectral response function (SRF) of the instrument detection channel. The simulated results under the clear air condition have quite high precision. In addition, the assimilation application of satellite data in current NWP model should be under the clear air condition. However, the calculation precision under the cloudy and rain conditions needs to be improved because of the complexity of the radiative effect of water. Several key problems remain unsolved in the simulation of cloud atmosphere radiance in the infrared radiative (IR) channels. Firstly, there are lots of uncertain parameters for both the numerical forecast and the radiative transfer model in the cloud region. Secondly, the results of the satellite observation may not be consistent with the results of the numerical prediction. Thirdly, the atmospheric temperature and humidity in the clear sky and cloudy sky have obvious structural differences in the vertical direction. Lastly, the energy of infrared radiance in the cloudy region is more nonlinear than other atmospheric parameters[12]. Based on the advantages and disadvantages of various models, HIRTM considers the atmospheric transmittance caused by molecular absorption, cloud absorption, and scattering of water under the condition of cloud. Further, compared with the simple cloud region simulation, HIRTM considers the cloud scattering and absorption model based on the parameters of the effective cloud top, cloud phase, cloud particle size, and cloud optical thickness. QU et al.: Infrared Radiance Simulation and Application under Cloudy Sky Conditions Based on HIRTM 163

2.1. Calculation of Clear Air Atmospheric Radiance The calculation of clear sky atmospheric radiance is relatively mature. The atmospheric state can be interpolated into the vertical pressure layer of the radiative transfer model from the NWP prediction field by the method of linear interpolation, and then the reliable and accurate infrared brightness temperature calculation can be carried out. The calculation of atmospheric transmittance model is critical among the calculation of clear sky radiance. So, based on the typical high temperature humidity profile library and the corresponding accurate transmittance, the linear regression model is applied to calculate the transmittance coefficient to realize the rapid calculation of the atmospheric transmittance. Moreover, the atmospheric transmittance, radiance, and brightness temperature could be calculated according to the real-time profile. HIRTM divides the atmospheric vertical layer from 0.005 hPa to 1100 hPa into 101 layers. The atmospheric transmittance calculation model is from the stand-alone 98 radiative transfer algorithm (SARTA)[13],[14].

2.2. Cloud Radiative Model In the cloudy sky, different shapes and sizes of cloud and particles have much complex scattering and absorption characteristics in different optical wavebands. Therefore, a lookup table is built, which includes different types of cloud optical thicknesses, cloud particle sizes, and transmittance and reflectivity functions. The model assumes that the cloud is in a plane parallel, uniform, and horizontal isothermal layer in a given perspective. In addition, the model also offers a given angle of view to calculate the absorption of energy by the mixture including nitrogen, oxygen, water vapor, ozone, and carbon dioxide. The main elements of the model include the cloud types, equivalent cloud top pressure height, 0.55-μm cloud optical thickness, and cloud particle size. In the model of cloud absorption and scattering, cloud is divided into water cloud and ice cloud. Water cloud is assumed to be a spherical droplet. Meanwhile, the classical Lorenz-Mie theory is applied to calculate its single scattering characteristics. For ice cloud, the single scattering properties[14] are calculated according to the particle size assumption of large particles (more than 300 μm), hexagonal geometric crystals (50 μm to 300 μm), and small particles of supercooled water ice droplets (below 50 μm). The visible light thickness is set to be 0.55 m. The optical thickness of the infrared band can be calculated by the mean extinction coefficient:

Qe( ) = vis Q( ) =2 (1) h i where Qe( ) is the volume average extinction coefficient, vis is the visible optical thickness at 0.55 m, and Q( ) is the cloud optical thickness for the IR channel. In this model, the discrete coordinate radiative transmission (DISORT) method is used to establish the parameter lookup table based on different cloud optical thicknesses, cloud sizes, single scattering albedo, and asymmetric factors, which can be checked[14] in accordance with the radiative characteristics of ice clouds and water clouds. Through coupling of the optical thickness of the clear sky and the optical effect of the cloud, the cloud radiance of the given IR channel can be calculated. The optical effects of cloud can be obtained based on the principles of radiative transfer and the lookup tables of reflection and transmission of the cloud. Hence, the cloud radiative transfer equation can be obtained for radiance simulation of the given infrared channels. The effective single cloud is only considered in the cloud model, which can simulate the cloud region radiance under multiple cloud conditions[11],[13],[15]. If we consider only the radiative effects of single layer cloud (Fig. 1), the radiative transfer equation can be described by

R=R0FT C + RC C + R1 + R1#FR C (2) where R0 indicates the upward of cloud top radiance; FT is the cloud transmittance; RC is the cloud radiance; 164 JOURNAL OF ELECTRONIC SCIENCE AND TECHNOLOGY, VOL. 17, NO. 2, JUNE 2019

R1 is the upward of cloud radiance; C is the air transmittance of the cloud top; R1# indicates the

↓ downward of cloud top atmosphere radiance; FR is R0FTτC R0τC R1 R1 FRτC the cloud reflection. Single Height layer Focused on HIRTM, the attenuation function of cloud cloud radiance is simplified for transmission and reflection. DISORT is used to calculate the transmittance and reflectivity lookup table related to the Surface effective radius of the particles, optical thickness, content of ice cloud and water cloud, angle of Fig. 1. Diagram of an effective single layer cloud observation, and center wavelength. After comparing assumption. with the actual the moderate resolution imaging spectroradiometer (MODIS) observations, HIRTM has the following advantages (compared with RTTOV and CRTM): The calculation speed is several times than that of RTTOV and CRTM; the calculation precision[11] is far superior to that of RTTOV; the cold deviation is better than that of CRTM.

2.3. Simulation Processes HITRM was designed for the infrared hyper-spectral channel, but it was also applied to the multispectral channel[12]. HITRM was applied to the simulation of the infrared channel for Himawari-8 advanced himawari imager (AHI). The central wavelength and spatial resolution of AHI/ABI/AGRI are show in Table 1.

Table 1: Central wavelength and spatial resolution of AHI/ABI/AGRI bands

Himawari-8/AHI GOES-R/ABI FY-4A/AGRI Center Spatial Center Spatial Center Spatial Channel Channel Channel wavelength (μm) resolution (km) wavelength (μm) resolution (km) wavelength (μm) resolution (km) 1 0.46 1.0 1 0.47 1.0 1 0.46 1.0 2 0.51 1.0 2 0.64 0.5 2 0.64 0.5 to 1.0 3 0.64 0.5 3 0.86 1.0 3 0.86 1.0 4 0.86 1.0 4 1.37 2.0 4 1.38 2.0 5 1.60 2.0 5 1.60 1.0 5 1.61 2.0 6 2.30 2.0 6 2.20 2.0 6 2.25 2.0 to 4.0 7 3.90 2.0 7 3.90 2.0 7 3.80 (high) 2.0 8 6.20 2.0 8 6.29 2.0 8 3.80 (low) 4.0 9 7.00 2.0 9 6.90 2.0 9 6.50 4.0 10 7.30 2.0 10 7.30 2.0 10 7.20 4.0 11 8.60 2.0 11 8.40 2.0 11 8.50 4.0 12 9.60 2.0 12 9.60 2.0 – – – 13 10.40 2.0 13 10.30 2.0 – – – 14 11.20 2.0 14 11.20 2.0 12 11.00 4.0 15 12.30 2.0 15 12.30 2.0 13 12.00 4.0 16 13.30 2.0 16 13.30 2.0 14 13.30 4.0

Besides the spectral response function of the instrument, the real-time atmospheric state and optical transmission path need to be determined in the cloud simulation of the geostationary meteorological satellite. The real-time state of the atmosphere comes from the numerical forecast data, and the transmission path mainly considers the influence of the zenith angle of the satellite. In this paper, the output product of WRF is used as the input parameters of the numerical forecast data of HIRTM. As a popular mesoscale prediction model, WRF3.5.1 is applied and the initial field is used as the input of the global forecast field of National Environmental Prediction Center. It is necessary to calculate the atmospheric transmittance of the channel according to the spectral response QU et al.: Infrared Radiance Simulation and Application under Cloudy Sky Conditions Based on HIRTM 165 function of the instrument, the transmission function, and the albedo function of different types of clouds (cirrus cloud, water cloud, and ice cloud) under different wavelengths and optical thicknesses. These parameters participated in the lookup table for real-time computation, which effectively improves the computational efficiency. With WRF simulation results, the parameters, such as the wind speed, mixture ratio of cloud, water, and ice (see Table 2), are obtained to calculate the effective particle radius of the type of cloud water available. According to the effective particle size and mixing ratio, the optical thickness of the visible light is calculated including water, ice, rain, snow, snow pellets (shotgun, soft hail), and liquid path. The size and the cloud pressure of the Table 2: List of parameters effective particles are calculated, including the cloud, Number Parameters Physics parameters liquid cloud, solid cloud, water cloud, ice cloud, rain 1 U10 10 m wind speed U-vector cloud, snow cloud, and cloud state (The cloud state is 2 V10 10 m wind speed V-vector 3 PSFC Surface pressure divided into water cloud and ice crystal cloud). 4 TSK Surface temperature The Strow-Woolf model[16],[17] of the middle dry gas, 5 T2 2 m temperature water vapor, and ozone in the rapid transfer model of 6 Q2 2 m mixing ratio 7 HGT Surface height satellite instrument is established based on the satellite 8 LU_INDEX Surface category number zenith angle, and then the transmittance coefficients of 9 QVAPOR Mixing ratio of water vapor 10 QCLOUD Mixing ratio of water cloud the 101 layers are calculated. The water formation 11 QRAIN Mixing ratio of water rain cloud in the atmosphere is used, then the transmissivity 12 QICE Mixing ratio of ice and reflectivity of the cloud are obtained. 13 QSNOW Mixing ratio of snow

Calculating Transmissivity Calculating the Albedo satellite zenith function of the effective coefficient angle instrument channel particle radius

Building strow- Calculating the Woolf model optical thickness of effective particles

Calculating the channel transmittance Calculating the size coefficients of effective particles and the cloud state

Calculating the Calculating the transmittance and radiation of clear sky reflectivity of the cloud

Calculating the temperature of the top atmosphere and coupling radiation of clear sky and cloud

Fig. 2. Flow chart of cloud simulation process. 166 JOURNAL OF ELECTRONIC SCIENCE AND TECHNOLOGY, VOL. 17, NO. 2, JUNE 2019

Finally, the brightness temperature on the top of the atmosphere is calculated by coupling the clear sky and cloud radiance. The simulation calculation process is shown in Fig. 2.

3. Results and Discussion

From July 29 to July 31 in 2017, No. 1709th "NESAT" and No. 1710th tropical storm "HAITANG" landed in Fuqing, , China. The merger of and storms affected the eastern part of China[18]. Meanwhile, in the east of Japan, the 1705th typhoon "NORU” moved to the south of Ogasawara Islands, and was affected by the high temperature of the sea and the lower vertical wind shear. So, "NORU" began to form convective ring, and then the wind gradually opened. Finally, the “NORU” typhon gradually burst. Using the 0.5×0.5 degree forecast data of national centers for environmental prediction (NCEP) on July 28, 2017 as the input and running the WRF model, we obtain the 24 hour numerical prediction of WRF in 9 km horizontal resolution in China and the surrounding sea area (0° to 55° N, 70° E to 145° E). When using this data to perform the cloud simulation test on Himawari-8 AHI, we obtain the simulated cloud images with the resolution of 9 km. Fig. 3 shows the simulated brightness temperature (K) of Channels 9 to 16 of AHI at 0:00 o’clock on July 29, 2017. The data was all projected by Lambert. The tropical storm “HAITANG” was basically taken shape and “NESAT” was ready to land. Meanwhile, the cloud system around “NARU” began to develop vigorously with a clear spiral. The simulation results from different channels can clearly identify the spiral structure of the typhoon, the location of the outer cloud system, and the center of the typhoon. At the same time, the water vapor transport path can be clearly seen in the water vapor channel. Figs. 4 and 5 show the comparison of Channel 9 (7.1 μm) AHI and Channel 14 (11 μm) cloud simulation and observed data. The NWP data has 9 km resolution, which is consistent with the simulated data after the reprojection of the observation data. The simulated cloud images of the water vapor channel (Channel 9 to 11) and infrared channel (Channels 12 to 16) agree well with the actual observation clouds in the clear sky area, typhoon center area, and large range of rainfall cloud areas. It well reflects the transport of the large cloud system, structure of the typhoon cloud system, and position of the typhoon. However, the surrounding cloud system of the typhoon is weak and the structure is loose. Meanwhile, in the south of “NORU”, there is a large rain cloud cannot be simulated in all channels. The reason of the lack of simulation results may lay in two aspects: Firstly, the error of cloud parameters prediction in numerical prediction. The cloud top pressure is calculated from the effective particle size of the numerical prediction and the mixture ratio of cloud water. The accuracy of the cloud top pressure directly affects the number of the integral in the calculation process. Therefore, the top of the cloud is high, but the observational radiance of the cirrus clouds is mostly below the cirrus cloud, leading to the lower simulation results of the cirrus cloud than the observation. Secondly, at present, HIRTM is weak in simulating the thick ice cloud[11]. Therefore, further research attention should be paid to the cloud models and parameters used.

4. Conclusions

The earth is covered by cloud, and the meso scale weather system is often accompanied by occurrence, development, and decline of cloud. Therefore, the simulation of infrared radiance brightness temperature in cloudy conditions plays a key role in weather forecast. In this paper, the satellite cloud images were simulated by using HIRTM with Himawari-8 AHI, and then the QU et al.: Infrared Radiance Simulation and Application under Cloudy Sky Conditions Based on HIRTM 167

Longitude (°) Longitude (°) 70E 85E 100E 115E 130E 145E 160E 70E 85E 100E 115E 130E 145E 160E 50N 50N 40N 40N 30N 30N 20N 20N

Latitude (°) 10N Latitude (°) 10N EQ EQ Channel 9 Channel 10 187.5 211.8 236.2 260.5 183.4 211.5 239.6 267.7 Longitude (°) Longitude (°)

70E 85E 100E 115E 130E 145E 160E 70E 85E 100E 115E 130E 145E 160E 50N 50N 40N 40N 30N 30N 20N 20N

Latitude (°) 10N Latitude (°) 10N EQ EQ Channel 11 Channel 12

184.2 221.5 258.7 296.0 199.2 220.5 241.9 263.3

Longitude (°) Longitude (°) 70E 85E 100E 115E 130E 145E 160E 70E 85E 100E 115E 130E 145E 160E 50N 50N 40N 40N 30N 30N 20N 20N

Latitude (°) 10N Latitude (°) 10N EQ EQ Channel 13 Channel 14 184.4 222.5 260.5 298.6 189.4 228.1 266.8 305.5

Longitude (°) Longitude (°) 70E 85E 100E 115E 130E 145E 160E 70E 85E 100E 115E 130E 145E 160E 50N 50N 40N 40N 30N 30N 20N 20N

Latitude (°) 10N Latitude (°) 10N EQ EQ Channel 15 Channel 16 189.4 227.9 266.4 305.0 189.9 222.7 255.6 288.5 Fig. 3. Simulated brightness temperature (K) of AHI from Channels 9 to 16. atmospheric profile and cloud parameters obtained by WRF. Compared with the actual observation cloud image, the results showed that the HIRTM model could well simulate the brightness temperature under cloudy condition. The simulation of all kinds of cloud systems was consistent with the actual observation cloud image, though the error of the numerical forecast may lead to differences. While, cloud observation only reflects the past or real-time weather processes, and it is unable to predict the future weather situation. With HIRTM, the future satellite cloud map can be simulated, the movement and change 168 JOURNAL OF ELECTRONIC SCIENCE AND TECHNOLOGY, VOL. 17, NO. 2, JUNE 2019

Longitude (°) Longitude (°) 70E 85E 100E 115E 130E 145E 160E 70E 85E 100E 115E 130E 145E 160E 50N 50N

40N 40N

30N 30N

20N 20N Latitude (°) Latitude (°) 10N 10N

EQ EQ Channel 9 Channel 10 187.5 211.8 236.2 260.5 187.5 211.8 236.2 260.5 (a) (b)

Fig. 4. UTC 2017-7-29 00:00 simulated brightness temperature (K) for Himawari-8 AHI (Channel 9): (a) simulated data and (b) observed data.

Longitude (°) Longitude (°) 70E 85E 100E 115E 130E 145E 160E 70E 85E 100E 115E 130E 145E 160E 50N 50N

40N 40N

30N 30N

20N 20N Latitude (°) Latitude (°) 10N 10N

EQ EQ Channel 9 Channel 10 187.5 211.8 236.2 260.5 187.5 211.8 236.2 260.5 (a) (b) Fig. 5. UTC 2017-7-29 00:00 simulated brightness temperature (K) for Himawari-8 AHI (Channel 14): (a) simulated brightness temperature (K) and (b) observed brightness temperature (K). of the cloud system from the cloud map can be intuitively judged, and the condition of other systems, such as rainstorms and typhoons, can be predicted. Further, the weather forecast can be improved and the losses caused by disastrous weather can be reduced. Cloud simulation carried out by HIRTM can also check and improve the numerical forecast model. Compared with the actual cloud image, the model and parameter scheme can be evaluated, and the optimization and improvement can be made. By comparing the simulation and observation of AHI, the satellite observation can be simulated under the cloudy sky condition. To a certain extent, the infrared radiance can be used to predict the movement, development, and change of typhoon. In addition, the research not only provides an important section for the weather forecast, but also gives a method for the evaluation and improvement of the regional numerical model.

Acknowledgment

Thanks for the reviewers’ comments, the paper is significantly improved with their help.

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Jian-Hua Qu was born in Sichuan, China in 1976. He received the B.S. degree from Nanjing University, Nanjing, China in 1999. He is a senior engineer with Huayun Shinetek Company, China Meteorological Administration, Beijing, China. His research interests include data processing, image processing, remote sensing, and climate change.

Jun-Jie Yan was born in Hubei, China in 1980. She received the B.S. and M.S. degrees from Wuhan University, Wuhan, China in 2002 and 2005, respectively. She is the Chief Engineer with Huayun Shinetek Company, China Meteorological Administration. Her research interests include big data processing, image processing, weather forecast, and system architecture design.

Mao-Nong Ran was born in Sichuan, China in 1964. He received the B.S. degree from Nanjing University in 1986. He is a senior engineer with Huayun Shinetek Company, China Meteorological Administration. His research interests include data processing, image processing, and remote sensing in atmosphere and sea.