Active Microwave radiative transfer modelling and the related precipitation retrieving algorithm
Spaceborne active microwave radar has its unique advantages in measure global precipitation due to its capability of range/angular resolution as well as a high detection accuracy (compared to microwave radiometer).
Several satellite precipitation measuring missions have launched radars to the space,
e.g., Cloud Profiling radar (CPR, W-band) onboard CloudSat, Precipitation Radar (PR, Ku-band) onboard TRMM,
Dual-frequency Precipitation Radar (DPR, Ku-band and Ka-band) onboard GPM and a dual-frequency precipitation radar (Ku-band and Ka-band) onboard Chinese FengYun-3G (FY-3G),
which has been successfully launched in Apr. 17, 2023.
As a part of Prof. Li's group, my work is to develop better precipitation retrieving algorithm and products for FY-3G mission.
I use Cloud Resolution Model (CRM) and Radiative Transfer Model (RTM) to simulate radar reflectivities from cloud/precipitation events modelling,
which is compared with ground-based data and GPM-DPR products to evaluate the accuracy.
we particularly focus on studying the radiative properties of solid hydrometeor's shapes and its impacts to precipitation retrievals. We try to answer the following questions:
(1) What are the performances of simulations using different shape assumptions and using GPM DPR observations as a reference?
(2) What are the effects of the temperature-dependent shape assumption?
(3) What are the associated retrieval biases in the Z–R relationship, precipitation top height, and rain area when using different shape assumptions?
Related publication:
L. Mai, S. Yang, Y. Wang, R. Li.(2023). Impacts of Shape Assumptions on Z-R Relationship and Satellite Remote Sensing Clouds Based on Model Simulations and GPM Observations. Remote Sensing, 15(6), 1556.
Passive Microwave Radiation
Though it is difficult to measure vertical structures of clouds or precipitations when using passive microwave instructions,
they have several advantages, e.g., wide observation range, multiple satellites currently in orbit, low cost, and ease of manufacturing.
The most representative precipitation retrieval method is Bayesian algorithm,
which aims at optimization estimates based on the probability statistical relationships between surface rain-rate (R) and brightness temperatures (TBs) measured at top of the atmosphere.
This algorithm works well on ocean (dark background) surface precipitation retrieval, but the accuracy is pretty low when used on land (bright background).
The reason is that on land, the Microwave Land Surface Emissivity (MLSE) is large, exhibits significant spatiotemporal variability and is lack of accurate measurement.
The current Bayesian retrieval algorithm roughly distinguishes the land surface into 14 categories,
assuming the probability statistical relationships between R and TBs are not affected by MLSE within the category.
However, in fact, MLSEs are greatly influenced by surface material and moisture content, and they will change dramatically with the duration time of precipitation. These factors undoubtedly lead to significant errors in retrieval.
Therefore, I am trying to analyze the following issues:
(1) Characteristics of probabilities under the framework of two-dimensional Bayesian algorithm.
(2) How helpful is “accurate” MLSEs for surface rain-rate retrieval.
(3) The error of retrieved surface rain-rate due to MLSE error.
(Assist) Activate and Passive Combine Precipitation Retrieval
The Advantages of avtive microwave observation of precipitation are high spatial accuracy and observation accuracy,
and the advantages of passive microwave is its low cost and wide observation range.
Prof. Rui Li's group aims to build a precipitation retrieval algorithm based on Machine Learning. In this program, I am trying to provide the database by WRF and RT simulation,
which might be the most important part as it determines the retrieval accuracy of machine learning algorithm.
However, it is difficult to build a complete and unbiased dataset, so my junior sisters and I are currently working on it.
Soil-Vegetation-Atmosphere Radiation Transfer
I just recently found that the radiative transfer processes in soil, vegetation and atmosphere all contribute to the signal detected by instruments on satellite.
To be continue...
To be continue...
I am open to learn more and get involved in a different study.