Satellite Precipitation Evaluation over the U.S. Coastal Land/Water Using Gauge-Corrected Multi-Radar Multi-Sensor System by Yike Xu
Weather and climate over coastal regions have received increasing attention because of substantial population growth and sea-level rise. Coastal land/ocean rainfall estimates are available from satellite remote sensing. While numerous papers have been published on evaluating satellite precipitation datasets over land (including coastal land), fewer studies over the ocean. Recognizing precipitation radars over coastal land also cover coastal ocean (or lake water), we use the hourly Multi-Radar/Multi-Sensor System (MRMS) gauge-corrected precipitation product for three years (2018 to 2020) to evaluate the relative performance over the U.S. coastal land versus ocean (and water over the Great Lakes) of three widely used satellite-based precipitation products (IMERG, PERSIANN-CCS-CDR, and CMORPH). The study finds all three products overestimate heavy precipitation over water more than over land. PERSIANN-CCS-CDR has less discrepancy between land and water than IMERG Final and CMORPH. Additionally, the gauge adjustment in IMERG Final has a larger impact on the land.
Re-evaluation of Low Cloud Amount Relationships with Lower Tropospheric Stability and Estimated Inversion Strength by Lauren Cutler
Lower-tropospheric stability (LTS) and estimated inversion strength (EIS) have a widely accepted relationship with low cloud amount and are key observational foundations for understanding and modeling low-level stratiform clouds. Using the updated surface-based and satellite cloud data, here we revisit the relationships of low cloud amount with LTS and EIS. We find that low cloud amount is not as strongly correlated with LTS as established in the past. EIS does not provide a stronger correlation with low cloud amount than LTS over all eight regions (including the midlatitudes). Further analyzing the relationships between LTS and EIS with different types of low clouds, we find that there is a strong correlation of LTS and EIS with stratocumulus only. This explains the weaker correlation of total low cloud fraction to both LTS and EIS. These results also suggest the need to re-evaluate these relationships in Earth system models.
Understanding the Interannual Variability of 1 April SWE and Its Sensitivity to Temperature and Precipitation Over CONUS by Jorge Arevalo
Snow water equivalent (SWE) on 1 April is commonly used to estimate meltwater supply during the late spring and summer. Understanding its interannual variability will help better project future changes. Using 36 years of daily 4km UA-SWE data over the Conterminous U.S. (CONUS), we introduce a new quantity; i.e., the ratio between cumulative ablation and SWE accumulation (ABL/ACC), that linearly explains, on average, 32% more variability than October-March mean temperature (T) and cumulative precipitation (P) together. Forest cover affects the correlation of 1 April SWE with P&T, ABL/ACC, and other predictors at some elevation bands only. The sensitivity results of 1 April SWE to P or T from linear regressions and model sensitivity tests show similar spatial patterns (but different quantitative values) over the CONUS, with the highest sensitivity to P over the Sierra Nevada and the Rockies and to T over the Sierra Nevada and the Cascades.
Global 3D Horizontal Winds Retrieved from Hyper-Spectral Infrared Sounders by Amir Hassan Ouyed Hernandez
The global 3D distribution of horizontal wind (u,v) is the top priority satellite measurement gap for numerical weather prediction and for understanding atmospheric dynamics and its interaction with atmospheric physics and chemistry. Yet the global distribution of horizontal winds remains mostly unobserved. We developed an algorithm to derive winds from tropics and mid-latitudes across 49 vertical levels from two polar satellites (NOAA-20 and Suomi NPP) carrying high resolution sounding instruments (Cross-track Infrared Sounder (CrIS)). Our algorithm yields ~1000 to ~10000 wind measurements per level per day. Comparisons with measurements from weather balloons show that our wind retrievals can outperform existing satellite wind products in accuracy and vertical resolution.