# Impact of the Assimilation of Multi-Platform Observations on Heavy Rainfall Forecasts in Kong-Chi Basin, Thailand

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Rain Events Identification

#### 2.2. Model Setup

#### 2.3. Assimilation Experiments

## 3. Observations Network

## 4. Results and Discussion

#### 4.1. Evaluation of Rainfall Forecast

#### 4.2. Evaluation of Temperature Forecast

## 5. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

3DVAR | three-dimensional variational |

4DVAR | four-dimensional variational |

ACC | accuracy |

AIREP | other conventional aircraft reports |

AWS | Automatic Weather Stations |

BIAS | bias score |

BMJ | Betts-Miller-Janic |

CNTL | without data assimilation; non-data assimilation |

DA | data assimilation |

GFS | Global Forecasting System |

GTS | Global Telecommunication System |

KCB | Kong-Chi basin |

MAE | mean absolute error |

METAR | dew point temperature reported by aircrafts |

MMM | Mesoscale and Microscale Meteorology Laboratory |

NCAR | National Center for Atmospheric Research |

NCEP | National Centers for Environmental Prediction |

NMC | National Meteorological Centre |

NWP | Numerical Weather Prediction |

OBS | observations |

Probability distribution function | |

QSCAT | Windspeed Scattometer data |

RRTM | Rapid Radiative Transfer Model |

SATOB | satellite moisture bogus reports |

SEA | South East Asia |

SNYOP | land surface observations |

SOUND | upper-air observations |

T | temperature |

TS | threat scores |

USGS | United States Geological Survey |

WMO | World Meteorological Organization |

WRFDA | Weather Research and Forecasting model data assimilation |

YSU | Yonsei University Scheme |

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**Figure 1.**Geographical distribution of the rainfall accumulation on (

**a**) 28 July 2017 and, (

**b**) 30 August 2019.

**Figure 2.**Model domains used for assimilation experiments. (

**a**) Model domains. Domains 1–3 denote, respectively, d01, South East Asia used to assess observed assimilation, d02, Thailand, used to assess observed assimilation, and d03, the Kong-Chi basin (KCB), used for evaluation; (

**b**) observations for evaluating the KCB.

**Figure 3.**Spatial distribution of the observations used in assimilation experiment PREPBUFR (red dot) + AWS (blue dot) for South East Asia at 00UTC 25 July 2017. The number of observations assimilated in the WRFDA model and that of total observations available at the time of assimilation are indicated at the top right corner of each plot.

**Figure 4.**Differences (model observation) in accumulated rainfall in both experiments of the SONCA event. (

**a**) DAALL-72h, (

**b**) DAAWS-72h, (

**c**) DAALL-48h, and (

**d**) DAAWS-48h.

**Figure 5.**Differences (model observation) in accumulated rainfall in both experiments of the PODUL event. (

**a**) DAALL-72h, (

**b**) DAAWS-72h, (

**c**) DAALL-48h, and (

**d**) DAAWS-48h.

**Figure 7.**BIAS and TS score for 3 h of accumulated rainfall with different thresholds and the gain in accuracy (%; line) from both assimilation experiments for the SONCA event. BIAS for (

**a**,

**b**) has 72 h and 48 h lead times; TS for (

**c**,

**d**) has 72 h and 48 h lead times.

**Figure 8.**BIAS and TS score for 3 h of accumulated rainfall with different thresholds and the gain in accuracy (%; line) from both assimilation experiments for the PODUL event. BIAS for (

**a**,

**b**) has 72 h and 48 h lead times; TS for (

**c**,

**d**) has 72 h and 48 h lead times.

**Figure 9.**Geographical distribution of the DAALL and DAAWS skill scores average with 48 h lead time for the SONCA event. (

**a**) ACC of DAALL, (

**b**) ACC of DAAWS, (

**c**) BIAS of DAALL, (

**d**) BIAS of DAAWS, (

**e**) TS of DAALL, and (

**f**) TS of DAAWS.

**Figure 10.**Geographical distribution of the DAALL and DAAWS skill scores average with 48 h lead time for PODUL event. (

**a**) ACC of DAALL, (

**b**) ACC of DAAWS, (

**c**) BIAS of DAALL, (

**d**) BIAS of DAAWS, (

**e**) TS of DAALL, and (

**f**) TS of DAAWS.

**Figure 11.**Scatterplots of temperature (T) of the observed vs. the 72 h and 48 h lead times for the SONCA event. (

**a**,

**c**) DAALL; (

**b**,

**d**) DAAWS. The linear regression line is plotted, and its slope m is presented along with the correlation coefficient r in the top-left corner of each scatterplot.

**Figure 12.**Scatterplots of temperature (T) of the observed vs. the 72 h and 48 h lead times for PODUL event. (

**a**,

**c**) DAALL; (

**b**,

**d**) DAAWS. The linear regression line is plotted, and its slope m is presented along with the correlation coefficient r in the top-left corner of each scatterplot.

**Figure 13.**Geographical distribution of the temperature statistical scores average with a 48 h lead time for the SONCA event. (

**a**,

**b**) Correction and MAE of DAALL T, and (

**c**,

**d**) correction and MAE of DAAWS T.

**Figure 14.**Geographical distribution of the temperature statistical scores average with a 48 h lead time for the PODUL event. (

**a**,

**b**) Correction and MAE of DAALL T, and (

**c**,

**d**) correction and MAE of DAAWS T.

WRF Model Setup | |||
---|---|---|---|

Configurations | Domain01 | Domain02 | Domain03 |

Regions | SEA | Thailand | KCB |

South-East Grids (grid points) | 126 × 110 | 181 × 196 | 268 × 304 |

No. of vertical levels | 28 | 28 | 28 |

Grids resolution (km) | 27 | 9 | 3 |

Microphysics | Eta microphysics | ||

Cumulus convection | BMJ | ||

Surface layer | Janjic’ Eta Model scheme | ||

Land surface model | Noah Land Surface | ||

Planet boundary layer | YSU | ||

Shortwave radiation | Dudhia scheme | ||

Longwave radiation | RRTM |

Event | Lead-Time (h) | Experiment | Initialization and Ending Time |
---|---|---|---|

SONCA | 72 | DAALL | 00UTC 25 July 2017–00UTC 29 July 2017 |

DAAWS | 00UTC 25 July 2017–00UTC 29 July 2017 | ||

48 | DAALL | 00UTC 26 July 2017–00UTC 29 July 2017 | |

DAAWS | 00UTC 26 July 2017–00UTC 29 July 2017 | ||

PODUL | 72 | DAALL | 00UTC 27 August 2019–00UTC 31 August 2019 |

DAAWS | 00UTC 27 August 2019–00UTC 31 August 2019 | ||

48 | DAALL | 00UTC 28 August 2019–00UTC 31 August 2019 | |

DAAWS | 00UTC 28 August 2019–00UTC 31 August 2019 |

Statistics | Definition | Range |
---|---|---|

Precipitation Accuracy (ACC) | The fraction of correct forecasts | 1–100%, where 100% is a perfect score |

Bias score (BIAS) | The number of correct forecasts and the number of each threshold of observed rainfall | From $-\infty $ to $\infty $, where 1 is a perfect score BIAS < 1 underforecast BIAS > 1 overforecast |

Threat score (TS) | The fraction of correct forecasts | 0–1, where 1 is a perfect score |

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**MDPI and ACS Style**

Thodsan, T.; Wu, F.; Torsri, K.; Khampuenson, T.; Yang, G.
Impact of the Assimilation of Multi-Platform Observations on Heavy Rainfall Forecasts in Kong-Chi Basin, Thailand. *Atmosphere* **2021**, *12*, 1497.
https://doi.org/10.3390/atmos12111497

**AMA Style**

Thodsan T, Wu F, Torsri K, Khampuenson T, Yang G.
Impact of the Assimilation of Multi-Platform Observations on Heavy Rainfall Forecasts in Kong-Chi Basin, Thailand. *Atmosphere*. 2021; 12(11):1497.
https://doi.org/10.3390/atmos12111497

**Chicago/Turabian Style**

Thodsan, Thippawan, Falin Wu, Kritanai Torsri, Thakolpat Khampuenson, and Gongliu Yang.
2021. "Impact of the Assimilation of Multi-Platform Observations on Heavy Rainfall Forecasts in Kong-Chi Basin, Thailand" *Atmosphere* 12, no. 11: 1497.
https://doi.org/10.3390/atmos12111497