AbstractDetection of shallow warm rainfall remains a critical source of uncertainty in remote sensing of precipitation, especially in regions of complex topographic and radiometric transitions such as mountains and coastlines. To address this problem, a new algorithm to detect and classify shallow rainfall based on space-time dual-frequency correlation (DFC) of concurrent W- and Ka-band radar reflectivity profiles is demonstrated using ground-based observations from IPHEx in the Appalachian Mountains (MV), US, and BAECC in Hyytiala (TMP), Finland. Detection is successful with false alarm errors of 2.64% and 4.45% respectively for MV and TMP, corresponding to one order of magnitude improvement over the skill of operational satellite-based radar algorithms in similar conditions. Shallow rainfall is misclassified 12.5% of the time at MV, but all instances of low-level reverse orographic enhancement are detected and classified correctly. The classification errors are 8% and 17% respectively for deep and shallow rainfall in TMP, the latter linked to reflectivity profiles with dark-band, whereas insufficient radar sensitivity to light rainfall (<2 mm/h) remains the major source of error. The potential utility of the algorithm for satellite-based observations in mountainous regions is explored using an observing system simulation (OSS) of concurrent CloudSat-CPR and GPM-DPR during IPHEx, and concurrent satellite observations over Borneo. The results suggest that integration of the methodology in existing regime-based classification algorithms is straightforward, and can lead to significant improvements in the detection and identification of shallow precipitation.
Journal of Atmospheric and Oceanic Technology – American Meteorological Society
Published: Jul 19, 2017
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera