# Applicability of Multi-Frequency Passive Microwave Observations and Data Assimilation Methods for Improving NumericalWeather Forecasting in Niger, Africa

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## Abstract

**:**

## Nomenclature

AMSR | Advanced Microwave Scanning Radiometer |

AMSR-E | AMSR on Earth Observing System |

AMSR-2 | AMSR-2nd generation on GCOM-W1 satellite |

NCEP | National Centers for Environmental Prediction |

LPRM | NASA Land Parameter Retrieval Model |

ARPS | The Advanced Regional Prediction System |

CALDAS | Coupled Atmosphere and Land Data Assimilation System |

EnKF | Ensemble Kalman Filter |

CMDAS | Cloud Microphysics Data Assimilation System |

LDAS | Land Data Assimilation Sysatem |

LDAS-A | LDAS coupled with an Atmospheric model |

NWP | Numerical Weather Prediction |

IR data | InfraRed data |

SiB2 | Simple Biosphere model version-2 |

TRMM | Tropical Rainfall Measuring Mission |

## 1. Introduction

## 2. Dataset, Models and Method

#### 2.1. Dataset

#### 2.1.1. Initial and Boundary Conditions

#### 2.1.2. AMSR-E Brightness Temperature Data

#### 2.1.3. In Situ Data

#### 2.1.4. Satellite-Derived Soil Moisture Products

#### 2.1.5. Satellite Cloud and Rainfall Products

#### 2.2. Models

_{b}at the satellite level is given by [18]:

_{p}is the surface reflectivity and T

_{s}is the surface physical temperature (K), ω

_{c}is the single-scattering albedo of the canopy, τ

_{c}is the vegetation optical thickness and T

_{c}is the canopy temperature (K).

_{1}, w

_{2}, w

_{3}]

^{T}as a state variable and the first estimate, for which w

_{1}, w

_{2}and w

_{3}are the soil moisture contents of the surface, root and deep soil layers, respectively. The first estimate is used to create an ensemble of size (N) by adding pseudo-random noise with known statistics. By dropping the time notation, each member of state variable X

_{i}is given by

_{i}is the random error vector of each member obtained from a multivariate Gaussian distribution with zero mean and error covariance matrix P, and X̄ is the expectation of the first estimate X. In the forecast step, the forecast state member ${X}_{i}^{f}$ is determined from the nearest analysis state member ${X}_{i}^{a}$ according to

_{i}is the model error vector of each member, obtained from a multivariate Gaussian distribution with zero mean and error covariance matrix Q.

_{o}is the observation, R is the observation error covariance and v

_{i}is a random error vector of the observation with zero mean and covariance matrix R.

#### 2.3. Method

- As shown in Figure 1, the land-atmosphere mesoscale model (ARPS-SiB2) was established using initial and boundary conditions from NCEP-FNL data.
- The ARPS model was integrated for a predefined period (10 min) and the calculated atmospheric forcing data transferred to the SiB2 model.
- At the beginning of the SiB2 integration, the ensemble (50 members) of soil moisture profiles was generated. SiB2 was executed independently for each ensemble member of the soil moisture profile, retaining the same model parameters and atmospheric forcing. At the end of the SiB2 calculation, the mean values of the updated soil state and fluxes were computed and fed back to the ARPS model as the lower boundary conditions of the atmospheric model. Then, the ARPS-SiB2 model was integrated forward in time.
- At times, when AMSR-E observations were available, the brightness temperatures at 6.9 and 10.65 GHz were perturbed to produce an ensemble of observations with prescribed statistics. The SiB2-driven ensemble of soil moisture profiles, surface temperature, and canopy temperature were used to obtain the simulated brightness temperatures using the forward microwave radiative transfer model. The EnKF calculated the assimilated soil moisture profiles using simulated and observed brightness temperatures, as shown in Equation (4). In the case of soil moisture assimilation (no cloud assimilation), the updated soil state and fluxes were fed back to the ARPS model and the ARPS-SiB2 model was integrated forward in time.
- In the case of cloud data assimilation, CMDAS was activated as soon as the LDAS completed the soil moisture assimilations. The control variables (profiles of temperature, specific humidity, pressure, air density, mixing ratio of cloud water, rain water, hail, snow, and cloud ice) were obtained from ARPS as an initial state to run the model operator (Lin’s ice microphysics [24]). The 4-stream fast model calculated the modeled brightness temperatures for 89 GHz at the satellite level by considering the land surface as the lowest boundary. Land surface emissivity was calculated using assimilated soil moisture content. The Shuffle Complex Evolution scheme was used to estimate the assimilated cloud parameters (i.e., cloud liquid water, snow, and rain) by minimizing the cost function calculated between the modeled and observed brightness temperatures. Then, the updated soil state and fluxes were fed back to the ARPS-SiB2 model.
- Finally, with the reinitialized land surface and atmospheric conditions, the ARPS-SiB2 model was integrated forward in time to predict the land and atmospheric evolution until the next AMSR-E observations were available. The results from the ARPS-Sib2 model were recorded at 30-min intervals.

## 3. Experiment Descriptions

#### Study Domain and Model Configuration

^{−2}, respectively.

## 4. Results and Discussions

#### 4.1. Distribution of Surface Soil Moisture

#### 4.2. Comparisons of Cloud Condensate With Satellite IR Observations

#### 4.3. Evaluation of Land–Atmosphere Interactions

#### 4.4. Evaluation oF Rainfall Forecast

## 5. Conclusions

## Acknowledgments

## Conflicts of Interest

**Author Contributions**Mohamed Rasmy coupled the satellite data assimilation systems within a mesoscale model, performed numerical simulations, analyzed satellite retrievals and model results, and wrote the manuscript. Xin Li contributed significantly for developing the data assimilation components and provided constructive comments and suggestions for coupling the components within a mesoscale model. Toshio Koike supervised the research work, outlined the design of data assimilation systems, and assisted with the system developments as well as the manuscript writing.

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**Figure 3.**Spatial distribution of volumetric surface soil moisture (m

^{3}/m

^{3}) at 0200 UTC on 6 June 2006; (

**a**) ARPS; (

**b**) CALDAS; (

**c**) dvanced Microwave Scanning Radiometer (AMSR)-E brightness temperature (K) observed at 6.9 GHz; (

**d**) Japan Aerospace Exploration Agency (JAXA) product; and (

**e**) National Aeronautics and Space Administration (NASA) product, respectively.

**Figure 4.**Hourly variation of integrated condensate (kg/m

^{2}) and IR data (K); first row: ARPS, second row: Land Data Assimilation System coupled with Atmospheric model (LDAS-A), third row: CALDAS, and fourth row: infrared (IR) brightness temperature.

**Figure 5.**Hourly variation of spatial correlations calculated from model simulated cloud top temperatures and IR cloud top temperatures for 6 June 2006.

**Figure 6.**Comparison of observed and model soundings at 1030 UTC on 6 June 2006; (

**a**) potential temperature (K) and (

**b**) specific humidity (g/kg).

**Figure 7.**Same as Figure 6 but for 2230 UTC on 6 June 2006; (

**a**) potential temperature (K) and (

**b**) specific humidity (g/kg).

**Figure 8.**Comparison of 6 hours (from 03UTC to 09UTC) accumulated rainfall (mm) obtained from (

**a**) ARPS; (

**b**) CALDAS; and (

**c**) TRMM, respectively.

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**MDPI and ACS Style**

Rasmy, M.; Koike, T.; Li, X.
Applicability of Multi-Frequency Passive Microwave Observations and Data Assimilation Methods for Improving NumericalWeather Forecasting in Niger, Africa. *Remote Sens.* **2014**, *6*, 5306-5324.
https://doi.org/10.3390/rs6065306

**AMA Style**

Rasmy M, Koike T, Li X.
Applicability of Multi-Frequency Passive Microwave Observations and Data Assimilation Methods for Improving NumericalWeather Forecasting in Niger, Africa. *Remote Sensing*. 2014; 6(6):5306-5324.
https://doi.org/10.3390/rs6065306

**Chicago/Turabian Style**

Rasmy, Mohamed, Toshio Koike, and Xin Li.
2014. "Applicability of Multi-Frequency Passive Microwave Observations and Data Assimilation Methods for Improving NumericalWeather Forecasting in Niger, Africa" *Remote Sensing* 6, no. 6: 5306-5324.
https://doi.org/10.3390/rs6065306