Studies of FY-3 Observations over the Past 10 Years: A Review

F2F-NN: A Field-to-Field Wind Speed Retrieval Method of Microwave Radiometer Data Based on Deep Learning

In this paper, we present a method for retrieving sea surface wind speed (SSWS) from Fengyun-3D (FY-3D) microwave radiation imager (MWRI) data. In contrast to the conventional point-to-point (P2P) retrieval methods, we propose a field-to-field (F2F) SSWS retrieval method based on the basic framework of a Convolutional Neural Network (CNN). Considering the spatial continuity and consistency characteristics of wind fields within a certain range, we construct the model based on the basic framework of CNN, which is suitable for retrieving various wind speed intervals, and then synchronously obtaining the smooth and continuous wind field. The retrieval results show that: (1) Comparing the retrieval results with the label data, the root-mean-square error (RMSE) of wind speed is about 0.26 m/s, the F2F-NN model is highly efficient in training and has a strong fitting ability to label data. Comparing the retrieval results with the buoys (NDBC and TAO) data, the RMSE of F2F-NN wind speed is less than 0.91 m/s, the retrieval accuracy is better than the wind field products involved in the comparison. (2) In the hurricane (Sam) area, the F2F-NN model greatly improves the accuracy of wind speed in the FY-3D wind field. Comparing five wind field products with the Stepped-Frequency Microwave Radiometer (SFMR) data, the overall accuracy of the F2F-NN wind data is the highest. Comparing the five wind field products with the International Best Track Archive for Climate Stewardship (IBTrACS) data, the F2F-NN wind field is superior to the other products in terms of maximum wind speed and maximum wind speed radius. The structure of the wind field retrieved by F2F-NN is complete and accurate, and the wind speed changes smoothly and continuously.

Forest Fire Detection of FY-3D Using Genetic Algorithm and Brightness Temperature Change

As one of China’s new generation polar-orbiting meteorological satellites, FengYun-3D (FY-3D) provides critical data for forest fire detection. Most of the existing related methods identify fire points by comparing the spatial features and setting thresholds empirically. However, they ignore temporal features that are associated with forest fires. Besides, they are difficult to generalize to multiple areas with different environmental characteristics. A novel method based on FY-3D combining the genetic algorithm and brightness temperature change detection is proposed in this work to improve these problems. After analyzing the spatial features of the FY-3D data, it adaptively detects potential fire points based on these features using the genetic algorithm, then filters the points with contextual information. To address the false alarms resulting from the confusing spectral characteristics between fire pixels and conventional hotspots, temporal information is introduced and the “MIR change rate” based on the multitemporal brightness temperature change is further proposed. In order to evaluate the performance of the proposed algorithm, several fire events occurring in different areas are used for testing. The Moderate-Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies/Fire products (MYD14) is chosen as the validation data to assess the accuracy of the proposed algorithm. A comparison of results demonstrates that the algorithm can identify fire points effectively and obtain a higher accuracy than the previous FY-3D algorithm.

Typhoon Warm-Core Structures Derived from FY-3D MWTS-2 Observations

In this study, the three-dimensional (3D) warm-core structures of the Northwest Pacific typhoons Francisco, Lekima, and Krosa in August 2019 are retrieved from the Fengyun-3D (FY-3D) microwave temperature sounder-2 (MWTS-2) observations of brightness temperature. Due to the lack of two window channels at 23.8 GHz and 31.4 GHz, an empirical cloud detection algorithm based on 50.3 GHz bias-corrected observations-minus-backgrounds is applied to obtain clear-sky observations for the multiple linear regression retrieval algorithm. The MWTS-2 cloud-affected channels 3–5 are not used to retrieve temperatures under cloudy conditions to eliminate low-tropospheric cold anomalies. The multiple linear regression coefficients are obtained based on MWTS-2 brightness temperatures and the temperatures from the European Centre for Medium-Range Weather Forecasts Reanalysis-5 (ERA5) in the training period of three weeks before the month of targeted typhoons. The proposed MWTS-2 warm-core retrieval can well capture the radial and vertical temporal evolutions of the temperature anomalies of the typhoons Francisco, Lekima, and Krosa. The sizes of the warm-core anomalies of typhoons Lekima and Krosa retrieved by the MWTS-2 are horizontally and vertically similar to and stronger than those of the ERA5. Compared with the ERA5 reanalysis in August 2019, the biases for MWTS-2 temperature retrievals are smaller than ±0.25 K, with root-mean-square errors (RMSEs) smaller than and 2.0 K at all altitudes. Additionally, the location of the 250-hPa maximum temperature anomaly retrieved by the MWTS-2 is closer to the best track than that of the ERA5. A weak warm-core around 200 hPa and a cold-core anomaly in the middle troposphere are also found in the outer rain bands region due to the effect of evaporation of rainfall.