# Evaluation and Improvement of FY-4A AGRI Quantitative Precipitation Estimation for Summer Precipitation over Complex Topography of Western China

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

## 3. Results

#### 3.1. Validation of NSMC QPE

#### 3.2. Improvement of QPE Algorithm Based on the FY-4A AGRI

#### 3.2.1. Cloud Classification

#### 3.2.2. Improvements of QPE Algorithm

_{P}), the secondary FY-4A QPE (QPE

_{S}) was calculated by adding TB temporal variation according to the following formula:

_{i,j}is the TB of LWIR channel 12 at the location of (i, j), and ΔT

_{i,j}, min(T

_{i,j}) and max(T

_{i,j}) are the average variability of T

_{i,j}, the minimum T

_{i,j}and the maximum T

_{i,j}within one hour at this location, respectively. In the processing of the correction, if QPE

_{S}has a value above 0.5 mm, then QPE

_{P}is replaced by QPE

_{S}; otherwise, QPE

_{P}remains unchanged.

_{0}is the instantaneous QPE at the current time (T

_{0}) obtained from the CST algorithm, and QPE

_{−1}and QPE

_{1}are the instantaneous QPE at the previous (T

_{−1}) and subsequent (T

_{1}) times, respectively. The dynamic time integration method is shown in Figure 6—the number of bars represents QPE; the blue and green bar represents QPE

_{−1}and QPE

_{1}, respectively, assuming that the five grey bars represent QPE

_{0}and remain constant. The ideal state is that the three QPEs are equal and, in this case, no processing is required. In addition to the ideal state, the processing methods of the other four cases are as follows: (1) when QPE

_{0}is greater than QPE

_{−1}and QPE

_{1}, the time integration weights for the previous and subsequent times increase; (2) when QPE

_{0}is smaller than QPE

_{−1}and QPE

_{1}, the time integration weight for the previous and subsequent times decrease; (3) when QPE

_{0}is greater than QPE

_{−1}but smaller than QPE

_{1}, the time integration weight decreases at the previous time yet increases at the subsequent time; (4) when QPE

_{0}is smaller than QPE

_{−1}but greater than QPE

_{1}, the time integration weight increases at the previous time yet decreases at the subsequent time.

_{I}) is calculated according to the following formula:

#### 3.3. Validations of Improved QPE Algorithm Based on the FY-4A AGRI

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A

Abbreviate | Full Name |
---|---|

AE | auto-estimator |

AGRI | Advanced Geosynchronous Radiation Imager |

AVHRR | Advanced Very High Resolution Radiometer |

CC | correlation coefficient |

CDF | cumulative distribution function |

CIMISS | China Integrated Meteorological Information Sharing Service platform |

CST | convective-stratiform technique |

FY-2 | Fenyun-2 |

FY-4A | Fengyun-4A |

GEO | geostationary |

GMSRA | GOES multispectral rainfall algorithm |

GOES-R | Geostationary Operational Environmental Satellite R series |

GPI | GOES precipitation index |

HE | hydro-estimator |

IR | infrared |

LEO | low earth orbiting |

LWIR | long-wave infrared |

LST | local standard time |

MAE | mean absolute error |

MARE | mean absolute relative error |

ME | mean error |

MICAPS | Meteorological Information Comprehensive Analysis and Processing System |

MRE | mean relative error |

MWIR | medium-wave infrared |

NESDIS | National Environmental Satellite Data and Information Service |

NOAA | National Oceanic and Atmospheric Administration |

NSMC | National Satellite Meteorological Center |

probability density function | |

QPE | quantitative precipitation estimation |

RG | rain gauge |

RMSE | root mean squared error |

SCaMPR | self-calibrating multivariate precipitation retrieval |

TB | brightness temperature |

WV | water vapor |

VIS | visible |

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**Figure 2.**Comparison between RG hourly precipitation and NSMC QPE of the FY-4A AGRI from June to August of 2020 in Western China: (

**a**) scatter density plot, (

**b**) PDF and CDF of ME and (

**c**) PDF and CDF of MRE.

**Figure 3.**Cloud classification of (

**a**) Method 1, (

**b**) Method 2, and (

**c**) Method 3 and (

**d**) the hourly precipitation at 08:00 LST on July 1, 2020, in Western China. (Str: Stratiform clouds, Thick Str: Thick stratiform clouds, Mixed: Mixed clouds, SCon: Shallow convective clouds, Con: Convective clouds, Deep Con: Deep convective clouds, W/L: Water/Land surface, Llc: Low level clouds, Mlc: Middle level clouds, As/Ns: Altostratus/Nimbostratus clouds, Cs: Cirrostratus clouds, Ci spi: Cirrus spissatus clouds).

**Figure 4.**The plots of the RG hourly precipitation vs (

**a**) the cloud-top temperature of LWIR channel 12 and (

**b**) the primary FY-4A QPE from June to August of 2020 (In Figure 4a, bottom and top of boxes denote the 25th and 75th percentiles, with the horizontal lines inside the box being the median value; the dotted lines represent the range of the adjacent value, which is the most extreme value that is not an outlier; the outliers are marked by crosses).

**Figure 5.**The relationship between the hourly precipitation and the hourly variation rate of TB for (

**a**) WV channel 9 and (

**b**) LWIR channel 12 from June to August of 2020.

**Figure 6.**Dynamic time integration method (

**a**) the ideal state, (

**b**) both smaller than, (

**c**) both greater than (

**d**) front smaller than rear greater than, and (

**e**) front greater than rear smaller than and dynamic time integration coefficient (the number of bars represents QPE, blue and green bars represent QPE

_{-1}and QPE

_{1}, respectively, assuming that the five grey bars represent QPE

_{0}and remain constant).

**Figure 7.**The scatter density plots of the RG hourly precipitation vs (

**a**) the secondary FY-4A QPE and (

**b**) the improved FY-4A QPE from June to August of 2020 in Western China.

**Table 1.**The two WV (water vapor) and four LWIR (long-wave infrared) channel settings of the FY-4A AGRI.

Channel Number | Central Wavelength/μm | Spatial Resolution/km | Temporal Resolution/min | Channel Name |
---|---|---|---|---|

C009 | 6.25 | 4.0 | 5~15 | WV |

C010 | 7.1 | 4.0 | 5~15 | WV |

C011 | 8.5 | 4.0 | 5~15 | LWIR |

C012 | 10.8 | 4.0 | 5~15 | LWIR |

C013 | 12.0 | 4.0 | 5~15 | LWIR |

C014 | 13.5 | 4.0 | 5~15 | LWIR |

**Table 2.**List of the validation statistical indices used to compare the QPE of the FY-4A AGRI and the GRs.

Statistical Index | Unit | Formula | Best Value |
---|---|---|---|

Mean Error (ME) | mm | $\frac{1}{n}{{\displaystyle \sum}}_{i=1}^{n}\left({P}_{i}-{G}_{i}\right)$ | 0 |

Mean Absolute Error (MAE) | mm | $\frac{1}{n}{{\displaystyle \sum}}_{i=1}^{n}\left|{P}_{i}-{G}_{i}\right|$ | 0 |

Mean Relative Error (MRE) | % | $\frac{1}{n}{{\displaystyle \sum}}_{i=1}^{n}\frac{\left({P}_{i}-{G}_{i}\right)}{{G}_{i}}\times 100\%$ | 0 |

Mean Absolute Relative Error (MARE) | % | $\frac{1}{n}{{\displaystyle \sum}}_{i=1}^{n}\frac{\left|{P}_{i}-{G}_{i}\right|}{{G}_{i}}\times 100\%$ | 0 |

Root Mean Squared Error (RMSE) | mm | $\sqrt{\frac{1}{n}{{\displaystyle \sum}}_{i=1}^{n}{\left({P}_{i}-{G}_{i}\right)}^{2}}$ | 0 |

Correlation Coefficient (CC) | NA | $\frac{{{\displaystyle \sum}}_{i=1}^{n}\left({G}_{i}-\overline{G}\right)\left({P}_{i}-\overline{P}\right)}{\sqrt{{{\displaystyle \sum}}_{i=1}^{n}{\left({G}_{i}-\overline{G}\right)}^{2}}\xb7\sqrt{{{\displaystyle \sum}}_{i=1}^{n}{\left({P}_{i}-\overline{P}\right)}^{2}}}$ | 1 |

**Table 3.**The used channel TB and TB difference thresholds for cloud classification based on the FY-4A AGRI.

Water/Land Surface (K) | Low Level Clouds (K) | Middle Level Clouds (K) | Altostratus/ Nimbostratus Clouds (K) | Cirrostratus Clouds (K) | Cirrus Spissatus Clouds (K) | Convective Clouds (K) | |
---|---|---|---|---|---|---|---|

C009 | 241 | 238 | 237 | 235 | 231 | 225 | 215 |

C010 | 254 | 251 | 248 | 245 | 239 | 230 | 217 |

C011 | 288 | 280 | 270 | 260 | 250 | 236 | 219 |

C012 | 290 | 281 | 270 | 260 | 248 | 234 | 217 |

C013 | 288 | 278 | 268 | 258 | 246 | 232 | 216 |

C014 | 261 | 257 | 252 | 246 | 238 | 228 | 216 |

C009−C014 | −20 | −19 | −15 | −11 | −7 | −3 | −1 |

C009−C013 | −47 | −40 | −31 | −23 | −15 | −7 | −1 |

C009−C012 | −50 | −42 | −33 | −25 | −17 | −9 | −2 |

C009−C011 | −48 | −41 | −33 | −25 | −19 | −11 | −4 |

C009−C010 | −13 | −12 | −11 | −10 | −8 | −5 | −2 |

C010−C012 | −37 | −30 | −22 | −15 | −10 | −4 | 0 |

C011−C014 | 27 | 22 | 18 | 14 | 12 | 8 | 3 |

C012−C014 | 29 | 23 | 18 | 14 | 10 | 6 | 1 |

C013−C014 | 27 | 21 | 16 | 12 | 8 | 4 | 0 |

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**MDPI and ACS Style**

Ren, J.; Xu, G.; Zhang, W.; Leng, L.; Xiao, Y.; Wan, R.; Wang, J.
Evaluation and Improvement of FY-4A AGRI Quantitative Precipitation Estimation for Summer Precipitation over Complex Topography of Western China. *Remote Sens.* **2021**, *13*, 4366.
https://doi.org/10.3390/rs13214366

**AMA Style**

Ren J, Xu G, Zhang W, Leng L, Xiao Y, Wan R, Wang J.
Evaluation and Improvement of FY-4A AGRI Quantitative Precipitation Estimation for Summer Precipitation over Complex Topography of Western China. *Remote Sensing*. 2021; 13(21):4366.
https://doi.org/10.3390/rs13214366

**Chicago/Turabian Style**

Ren, Jing, Guirong Xu, Wengang Zhang, Liang Leng, Yanjiao Xiao, Rong Wan, and Junchao Wang.
2021. "Evaluation and Improvement of FY-4A AGRI Quantitative Precipitation Estimation for Summer Precipitation over Complex Topography of Western China" *Remote Sensing* 13, no. 21: 4366.
https://doi.org/10.3390/rs13214366