藉由雷達模擬軟體QuickBeam探討雲與降水遙測之原理

A study of the remote sensing theories of cloud and precipitation by QuickBeam radar simulation software

紀雍華(Yong-hua Ji)

指導教授 ： 隋中興

國立中央大學/地球科學學院/大氣物理研究所/碩士(2008年)

摘要

雲與降水過程為大氣科學研究的重要議題，為了探討此些現象，裝載 有主被動微波感應器的衛星，如熱帶雨量測量任務衛星(Tropical Rainfall Measuring Mission, TRMM)和CloudSat衛星陸續發射；而模擬雲與降水過程 的高解析度模式的發展亦趨於完備。隨著衛星觀測資料的日益增加和模式 模擬雲值的可用度提升，如何有效應用衛星資料評估模式輸出值變得相當 重要。基於上述理由，此時需將模式模擬雲值轉換為有效雷達反射率因子 (Ze)，藉此模式轉換之Ze值便可和星載雷達觀測的Ze值比較。QuickBeam為 一氣象雷達模擬軟體，不論模擬的雷達電磁波傳輸方向為由天空往下或由 地表往上行進，只要藉由在常用的微波頻率範圍內給定水象粒子資訊，便 可模擬垂直的雷達反射率剖面。本研究目的著重於了解此雷達模擬軟體所 依據的原理，並討論其使用上的特性和限制。 QuickBeam的運算過程包含兩大部分：藉由水象粒子和氣象場變數資 料獲得粒子的微物理特性，並計算粒子的光學效應。首先在包含有雲滴至 降水粒子尺寸的雲內，藉由指定各種水象粒子的滴譜(drop-size distribution) 可計算得各種粒子的幾何截面積。接著計算反散射效率(Q(s))，在QuickBeam 中對所有尺寸的水象粒子(在此假設粒子均為球形)均使用米氏理論計算， 幾何截面積乘上Q(s)值並對粒子直徑積分後便可得雷達反射係數(η)。將η值 轉換為Ze值，扣除Ze值因水象粒子和空氣分子所造成之衰減，便得到考慮 衰減修正的雷達反射率。 本研究利用QuickBeam 內附包含六種水象粒子(雲水、雨、雪、撞併、 軟雹、雲冰)混合比的個案，檢驗QuickBeam 內的各項計算，並得到以下 結論：1)如何將觀測或模擬得的水象粒子混合比參數化為粒子數目( n(D)) 是QuickBeam 運算中的關鍵過程；2)對水象粒子及空氣分子的各種光學特 性之理解將有助於評估QuickBeam 的模擬結果。 然而，目前對上述議題(如雲與降水的微物理過程及粒子的光學特性) 之瞭解仍相當有限，這造成QuickBeam 在模擬雷達反射率時的限制。

關鍵字

米氏散射 ； 有效雷達反射率因子 ； 水象粒子

並列摘要

Clouds and precipitation is the central issue in atmospheric research.
Many passive and active microwave sensors have been developed and launched
(e.g. TRMM and CloudSat) for measuring clouds and precipitation from space.
High resolution models are also becoming better and more affordable. With
more satellite measurements and model-simulated clouds becoming available,
how to best utilize the data to evaluate model outputs becomes
ever-increasingly important. The above concern motivates an approach to
convert model simulated clouds into effective radar reflectivity factor (Ze) so it
can be compared directly with measured Ze by space-borne radar. QuickBeam
is a meteorological radar simulation package. It is designed to simulate vertical
radar reflectivity profiles from given hydrometeor information at any common
microwave frequency, from either the top-down (i.e. satellite-based radars such
as CloudSat) or the bottom-up. The purpose of this study is to understand
the theory and the limitations of the radar simulation software.
QuickBeam consists of two major parts: to derive cloud microphysical
properties from given cloud information, and to compute cloud radiative effects.
The first part is to get geometric cross-section area of all hydrometeors. This is
done by specifying a drop size distribution function for each bulk category of
clouds. The second part is to calculate the backscatter efficiency, Q(s), for cloud
particles of different size (normally diameter assuming clouds are spherical
shape) based on Mie theory. By multiplying the geometric cross-section area
with Q(s) and integrating it over the whole cloud spectrum, one gets the radar
reflective coefficient (η). Finally, one can convert η to Ze, and subtract the
attenuation by hydrometeor and atmospheric gases to get the corrected volume
reflectivity.
We applied QuickBeam to an example of given vertical profiles of mixing
ratio for six common bulk categories of clouds: cloud water, rain, cloud ice,
snow, aggregate, graupel. A careful examination of each step of the calculation
reveals the following:
(1) How to specify the number of hydrometeors ( n(D)) from a given cloud
mixing ratio is a critical step that requires extensive knowledge and observational
evidence in cloud microphysics.
(2) A good knowledge of spectrally dependent cloud optical properties is
required to assess the model results.
Our current understanding about the above issues is still quite limited.
This poses limitations of QuickBeam.

並列關鍵字

Mie scattering ； effective radar reflectivity factor ； hydrometeor

參考文獻

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