# Assimilating FY-4A Lightning and Radar Data for Improving Short-Term Forecasts of a High-Impact Convective Event with a Dual-Resolution Hybrid 3DEnVAR Method

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## Abstract

**:**

## 1. Introduction

## 2. Data and Methods

#### 2.1. Lightning Data

#### 2.2. Radar Data

#### 2.3. Data Assimilation Methods

#### 2.3.1. DVAR Method

#### 2.3.2. Dual-Resolution Hybrid 3DEnVAR Method

## 3. Experimental Design and Model Description

#### 3.1. Experimental Design

#### 3.2. Model Description

## 4. Results

#### 4.1. Analysis Field of Single-analysis Experiments

#### 4.1.1. Radar Reflectivity and Wind Field

#### 4.1.2. Water Vapor and Hydrometers

#### 4.2. Forecast Field

#### 4.2.1. The Single-analysis Experiments

#### 4.2.2. The Cycling Analysis Experiments

## 5. Summary and Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**The little global map (

**a**) and configuration of the WRF model domain with a grid spacing of 3 km (

**b**). The colors represent model terrain heights. The abbreviations for the Henan, Anhui, Hubei, Chongqing, Guizhou, Hunan, Jiangxi, Zhejiang, and Fujian Provinces are HeN, AH, HuB, CQ, GZ, HuN, JX, ZJ, and FJ, respectively.

**Figure 2.**The total cost function and summation of gradient norm as a function of the number of iterations in single-analysis experiments. The red solid line is the cost function (left Y-axis), and the blue dashed line is the gradient norm (right Y-axis). (

**a**,

**b**) Lightning data assimilation by 3DVAR (LDA_3DVAR) and hybrid 3DEnVAR (LDA_Hybrid_cov06), (

**c**,

**d**) radar data assimilation by 3DVAR (RDA_3DVAR) and hybrid 3DEnVAR (RDA_Hybrid_cov06), and (

**e**,

**f**) the combined lightning and radar data assimilation by 3DVAR (LRDA_3DVAR) and hybrid 3DEnVAR (LRDA_Hybrid_cov06).

**Figure 3.**The observed maximum radar reflectivity (MaxRadRef) and analyzed maximum reflectivity (MaxRef) horizontal wind vectors at z = 4 km at 0000 UTC on 30 June 2018 (analysis time). (

**a**) Observed maximum radar reflectivity interpolated onto the 3 km simulation domain, (

**b**) control run (CTL), and for single-analysis experiments: (

**c**,

**d**) lightning data assimilation by 3DVAR and hybrid 3DEnVAR, (

**e**,

**f**) radar data assimilation by 3DVAR and hybrid 3DEnVAR, (

**g**,

**h**) and the combined lightning and radar data assimilation by 3DVAR and hybrid 3DEnVAR. The white background area is the range of radar scanning in (

**a**). The black line AB in (

**a**) denotes the locations of the vertical cross-sections for subsequent figures.

**Figure 4.**Horizontal increments of vertical velocity (shaded contours) and wind vector (black vector arrows) from single-analysis experiments at z = 4 km (

**a**–

**c**) and vertical-cross sections of increments of vertical velocity and wind vector (

**e**–

**f**) at 0000 UTC 30 June 2018 (analysis time) along line AB in (

**a**). The line AB in (

**a**) is the same position as the line AB in Figure 3a. Lightning data assimilation by hybrid 3DEnVAR (

**b**,

**e**), radar data assimilation by 3DVAR (

**a**,

**d**) and hybrid 3DEnVAR (

**c**,

**f**). The black lines AB in (

**a**) denote the locations of the vertical cross-sections for (

**d**–

**f**).

**Figure 5.**The single-analysis increments of water vapor mixing ratio (${q}_{v}$) from 0 eta level to 15 eta level (

**a**–

**f**) were summed; graupel mixing ratio (${q}_{g}$) at 500 hPa (

**g**,

**j**), snow mixing ratio (${q}_{s}$) at 500 hPa (

**h**,

**k**), and rain mixing ratio (${q}_{r}$) at 700 hPa (

**i**,

**l**) at 0000 UTC 30 June 2018 (analysis time). (

**a**) Lightning data assimilation by 3DVAR. (

**d**,

**g**–

**i**) Lightning data assimilation by hybrid 3DEnVAR. (

**b**,

**e**) Radar data assimilation by 3DVAR and hybrid 3DEnVAR. (

**c**) The combined lightning and radar data assimilation by 3DVAR, and (

**f**,

**j**–

**l**) uses hybrid 3DEnVAR.

**Figure 6.**Vertical cross-sections of analysis increments of ${q}_{v}$ (blue shaded contour lines), ${q}_{g}$ (dark orchid contour lines), ${q}_{s}$ (orange contour lines) and ${q}_{r}$ (forest green contour lines) from single-analysis experiments at 0000 UTC 30 June 2018 (analysis time) along line AB in Figure 3a. (

**a**,

**d**) Lightning data assimilation by 3DVAR and hybrid 3DEnVAR, (

**b**,

**e**) radar data assimilation by 3DVAR and hybrid 3DEnVAR, and (

**c**,

**f**) the combined lightning and radar data assimilation by 3DVAR and hybrid 3DEnVAR.

**Figure 7.**The observed maximum radar reflectivity and forecasted maximum reflectivity and horizontal wind vectors from single-analysis experiments at 0300 UTC on 30 June 2018 (i.e., 3 h forecast). (

**a**) Observed maximum radar reflectivity interpolated onto the 3 km simulation domain (OBS), (

**b**) control run (CTL), lightning (

**c**,

**f**), radar (

**d**,

**g**), and combined lightning and radar data assimilation (

**e**,

**h**) by 3DVAR (

**c**–

**e**) and hybrid 3DEnVAR (

**f**–

**h**) showed that ${\beta}_{1}=0.4$, ${\beta}_{2}=0.6$. The combined lightning and radar data assimilation by hybrid 3DEnVAR (

**i**,

**j**) showed that ${\beta}_{1}=0.2$, ${\beta}_{2}=0.8$ and ${\beta}_{1}=0.0$, ${\beta}_{2}=1.0$, respectively.

**Figure 8.**The observed and forecasted 6 h accumulated precipitation for single-analysis experiments from 0000 UTC to 0600 UTC on 30 June 2018. (

**a**) Observed precipitation (OBS), (

**b**) control run (CTL), lightning (

**c**,

**f**), radar (

**d**,

**g**) and the combined lightning and radar data assimilation (

**e**,

**h**) by 3DVAR (

**c**–

**e**) and hybrid 3DEnVAR (

**f**–

**h**) showed that ${\beta}_{1}=0.4$, ${\beta}_{2}=0.6$. The combined lightning and radar data assimilation by hybrid 3DEnVAR (

**i**,

**j**) showed that ${\beta}_{1}=0.2$, ${\beta}_{2}=0.8$ and ${\beta}_{1}=0.0$, ${\beta}_{2}=1.0$, respectively.

**Figure 9.**The equitable threat score (ETS) (

**a1**–

**c3**) of the forecasted hourly accumulated precipitation for single-analysis experiments from 0000 UTC to 0600 UTC on 30 June 2018. The performance diagram (

**d1**–

**d3**,

**e1**–

**e3**) of 1 and 3 h forecast hourly accumulated precipitation for single-analysis experiments from 0000 UTC to 0100 UTC and 0200 UTC to 0300 UTC on 30 June 2018. (

**a1**–

**a3**) The lightning data assimilation experiments (LDA), (

**b1**–

**b3**) the radar data assimilation experiments (RDA), and (

**c1**–

**c3**) the combined lightning and radar data assimilation experiments. (

**a1**,

**b1**,

**c1**,

**d1**,

**e1**) the 1 mm threshold, (

**a2**,

**b2**,

**c2**,

**d2**,

**e2**) the 5 mm threshold, and (

**a3**,

**b3**,

**c3**,

**d3**,

**e3**) the 10 mm threshold. In each performance diagram plot, the lower-left corner represents no forecast skill and, similarly, the upper-right corner indicates perfect skill. Purple curves represent the critical success index (CSI), and the black dashed lines represent the frequency bias. The colored dots show the results for the experiments with legends shown at the bottom of the figure, the number inside each dot represents the forecast time in hours.

**Figure 10.**As in Figure 7, but for the cycling analysis experiments at 0400 UTC on 30 June 2018 (i.e., 3 h forecast). (

**a**) Observed maximum radar reflectivity interpolated onto the 3 km simulation domain (OBS), (

**b**) control run (CTL), lightning (

**c**,

**f**), radar (

**d**,

**g**) and the combined lightning and radar data assimilation (

**e**,

**h**) by 3DVAR (

**c**–

**e**) and hybrid 3DEnVAR (

**f**–

**h**) showed that ${\beta}_{1}=0.4$, ${\beta}_{2}=0.6$. The combined lightning and radar data assimilation by hybrid 3DEnVAR (

**i**,

**j**) showed that ${\beta}_{1}=0.2$, ${\beta}_{2}=0.8$ and ${\beta}_{1}=0.0$, ${\beta}_{2}=1.0$, respectively.

**Figure 11.**As in Figure 8, but for the cycling analysis experiments from 0100 UTC to 0700 UTC on 30 June 2018. (

**a**) Observed precipitation (OBS), (

**b**) control run (CTL), (

**b**) control run (CTL), lightning (

**c**,

**f**), radar (

**d**,

**g**) and the combined lightning and radar data assimilation (

**e**,

**h**) by 3DVAR (

**c**–

**e**) and hybrid 3DEnVAR (

**f**–

**h**) showed that ${\beta}_{1}=0.4$, ${\beta}_{2}=0.6$. The combined lightning and radar data assimilation by hybrid 3DEnVAR (

**i**,

**j**) showed that ${\beta}_{1}=0.2$, ${\beta}_{2}=0.8$ and ${\beta}_{1}=0.0$, ${\beta}_{2}=1.0$, respectively.

**Figure 12.**As in Figure 9, but for the cycling analysis experiments. (

**a1**–

**a3**) the lightning data assimilation experiments (LDA), (

**b1**–

**b3**) the radar data assimilation experiments (RDA), (

**c1**–

**c3**) the combined lightning and radar data assimilation experiments. (

**a1**,

**b1**,

**c1**,

**d1**,

**e1**) the 1 mm threshold, (

**a2**,

**b2**,

**c2**,

**d2**,

**e2**) the 5 mm threshold, (

**a3**,

**b3**,

**c3**,

**d3**,

**e3**) the 10 mm threshold.

**Table 1.**Abbreviations used for the experiments and descriptions of the experiments. All assimilation experiments were performed using single and cycling analysis, respectively. Single-analysis experiments were at 0000 UTC on 30 June, and cycling analysis experiments were performed from 0000 to 0100 UTC on 30 June with 15 min frequency. In single-analysis experiments, lightning frequency was accumulated from 2300 UTC on 29 June to 0030 UTC on 30 June, and in cycling analysis experiments, lightning frequency was accumulated for the 15 minutes before the analysis moment. To test the different hybrid coefficients, two sets of hybrid coefficients were used to assimilate the combined lightning and radar data. The label “cov06” represents ${\beta}_{1}=0.4$ and ${\beta}_{2}=0.6$, label “cov08” represents ${\beta}_{1}=0.2$ and ${\beta}_{2}=0.8$, and the label “cov10” represents ${\beta}_{1}=0.0$ and ${\beta}_{2}=1.0$.

Experiments | Data Assimilated | Data Assimilation Methods |
---|---|---|

CTL | None | None |

LDA_3DVAR | FY-4A LMI | 3DVAR method $({\beta}_{1}=1.0,{\beta}_{2}=0.0)$ |

LDA_Hybrid_cov06 | Hybrid 3DEnVAR method, $({\beta}_{1}=0.4$, ${\beta}_{2}=0.6)$ | |

RDA_3DVAR | Radar reflectivity and radial velocity | 3DVAR method $({\beta}_{1}=1.0,{\beta}_{2}=0.0)$ |

RDA_Hybrid_cov06 | Hybrid 3DEnVAR method, $({\beta}_{1}=0.4$, ${\beta}_{2}=0.6)$ | |

LRDA_3DVAR | FY-4A LMI, radar reflectivity, and radial velocity | 3DVAR method $({\beta}_{1}=1.0,{\beta}_{2}=0.0)$ |

LRDA_Hybrid_cov06 | Hybrid 3DEnVAR method, $({\beta}_{1}=0.4$, ${\beta}_{2}=0.6$) | |

LRDA_Hybrid_cov08 | Hybrid 3DEnVAR method, (${\beta}_{1}=0.2$, ${\beta}_{2}=0.8)$ | |

LRDA_Hybrid_cov10 | Hybrid 3DEnVAR method, $({\beta}_{1}=0.0$, ${\beta}_{2}=1.0)$ |

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## Share and Cite

**MDPI and ACS Style**

Liu, P.; Yang, Y.; Lai, A.; Wang, Y.; Fierro, A.O.; Gao, J.; Wang, C.
Assimilating FY-4A Lightning and Radar Data for Improving Short-Term Forecasts of a High-Impact Convective Event with a Dual-Resolution Hybrid 3DEnVAR Method. *Remote Sens.* **2021**, *13*, 3090.
https://doi.org/10.3390/rs13163090

**AMA Style**

Liu P, Yang Y, Lai A, Wang Y, Fierro AO, Gao J, Wang C.
Assimilating FY-4A Lightning and Radar Data for Improving Short-Term Forecasts of a High-Impact Convective Event with a Dual-Resolution Hybrid 3DEnVAR Method. *Remote Sensing*. 2021; 13(16):3090.
https://doi.org/10.3390/rs13163090

**Chicago/Turabian Style**

Liu, Peng, Yi Yang, Anwei Lai, Yunheng Wang, Alexandre O. Fierro, Jidong Gao, and Chenghai Wang.
2021. "Assimilating FY-4A Lightning and Radar Data for Improving Short-Term Forecasts of a High-Impact Convective Event with a Dual-Resolution Hybrid 3DEnVAR Method" *Remote Sensing* 13, no. 16: 3090.
https://doi.org/10.3390/rs13163090