Intercalibration of ASCAT Scatterometer Winds from MetOp-A, -B, and -C, for a Stable Climate Data Record

Scatterometers provide very stable ocean vector wind data records. This is because they measure the ratio of backscattered to incident microwave signal over the ocean surface as opposed to an absolute quantity (e.g., emitted microwave signal). They provide an optimal source of observations for building a long ocean vector wind Climate Data Record (CDR). With this objective in mind, observations from different satellite platforms need to be assessed for high absolute accuracy versus a common ground truth and for fine cross-calibration during overlapping periods. Here we describe the methodology for developing a CDR of ocean surface winds from the C-band ASCAT scatterometers onboard MetOp-A, -B, and -C. This methodology is based on the following principles: a common Geophysical Model Function (GMF) and wind algorithm developed at Remote Sensing Systems (RSS) and the use of in situ and satellite winds to cross-calibrate the three scatterometers within the accuracy required for CDRs, about 0.1 m/s at the global monthly scale. Using multiple scatterometers and radiometers for comparison allows for the opportunity to isolate sensors that are drifting or experiencing step-changes as small as 0.05 m/s. We detected and corrected a couple of such changes in the ASCAT-A wind record. The ASCAT winds are now very stable over time and well cross-calibrated with each other. The full C-band wind CDR now covers 2007-present and can be easily extended in the next decade with the launch of the MetOp Second Generation scatterometers.

Near-Earth space balloon-based multi-band airglow imager optic design

As a common tracer in the atmosphere, airglow can be used as an important means to study the interaction between the lower atmosphere, near space, and ionosphere. In the near-Earth space, the high-altitude balloon can realize long time flight, which makes the airglow detection realize both high range resolution and time resolution. In this paper, a balloon-based multi-band airglow imager is designed, which can observe OI (557.7 nm), Na (589.3 nm), OI (630.0 nm), and OH (720-910 nm) with annular field of view (30° inner ring and 80° outer ring), and its resolution is 500 m at 250 km. The multi-band airglow imager designed in this paper is equipped in the payload cabin and raised to above 30 km for flat flying for more than 6 h. The experimental results show that the imager worked normally, and airglow images were photographed and stored; the optical system can stand the harsh environment in the near space. The multi-band airglow imager designed in this paper will take part in other near-space exploration tasks in the future and obtain corresponding results.

Impact of Microphysical Parameterizations on Simulated Hurricanes—Using Multi-Parameter Satellite Data to Determine the Particle Size Distributions that Produce Most Realistic Storms

Understanding and forecasting hurricanes remains a challenge for the operational and research communities. To accurately predict the Tropical Cyclone (TC) evolution requires properly reflecting the storm’s inner core dynamics by using: (i) high-resolution models; (ii) realistic physical parameterizations. The microphysical processes and their representation in cloud-permitting models are of crucial importance. In particular, the assumed Particle Size Distribution (PSD) functions affect nearly all formulated microphysical processes and are among the most fundamental assumptions in the bulk microphysics schemes. This paper analyzes the impact of the PSD assumptions on simulated hurricanes and their synthetic radiometric signatures. It determines the most realistic, among the available set of assumptions, based on comparison to multi-parameter satellite observations. Here we simulated 2005′s category-5 Hurricane Rita using the cloud-permitting community Weather Research and Forecasting model (WRF) with two different microphysical schemes and with seven different modifications of the parametrized hydrometeor properties within one of the two schemes. We then used instrument simulators to produce satellite-like observations. The study consisted in evaluating the structure of the different simulated storms by comparing, for each storm, the calculated microwave signatures with actual satellite observations made by (a) the passive microwave radiometer that was carried by the Tropical Rainfall Measuring Mission (TRMM) satellite—the TRMM microwave imager TMI, (b) TRMM’s precipitation radar (PR) and (c) the ocean-wind-vector scatterometer carried by the QuikSCAT satellite. The analysis reveals that the different choices of microphysical parameters do produce significantly different microwave signatures, allowing an objective determination of a “best” parameter combination whose resulting signatures are collectively most consistent with the wind and precipitation observations obtained from the satellites. In particular, we find that assuming PSDs with larger number of smaller hydrometeors produces storms that compare best to observations.

A Neural Network-Based Rain Effect Correction Method for HY-2A Scatterometer Backscatter Measurements

The backscattering coefficients measured by Ku-band scatterometers are strongly affected by rainfall, resulting in a systematic error in sea surface wind field retrieval. In rainy conditions, the radar signals are subject to absorption by the raindrops in their round-trip propagation through the atmosphere, while the backscatter of raindrops raises the echo energy. In addition, raindrops give rise to roughness by impinging the ocean surface, resulting in an increase in the echo energy measured by a scatterometer. Under moderate wind conditions, the comprehensive impact of rainfall causes the wind speeds retrieved by the scatterometer to be higher than their actual values. The HY-2A scatterometer is a Ku-band, pencil-beam, conically scanning scatterometer. To correct the systematic error of the HY-2A scatterometer measurement in rainy conditions, a neural network model is proposed according to the characteristics of the backscatter coefficients measured by the HY-2A scatterometer in the presence of rain. With the neural network, the wind fields of the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data were used as the reference to correct the deviation in backscatter coefficients measured by the HY-2A scatterometer in rainy conditions, and the accuracy in wind speeds retrieved using the corrected backscatter coefficients was significantly improved. Compared with the cases of wind retrieval without rain effect correction, the wind speeds retrieved from the corrected backscatter coefficients by the neural network show a much lower systematic deviation, which indicates that the neural network can effectively remove the systematic deviation in the backscatter coefficients and the retrieved wind speeds caused by rain.