Impact of Vertical Wind Shear on Tropical Cyclone Rainfall

While tropical cyclone rainfall has a large axisymmetric component, previous observational and theoretical studies have shown that environmental vertical wind shear leads to an asymmetric component of the vertical motion and precipitation fields. Composites consistently depict a precipitation enhancement downshear and also cyclonically downwind from the downshear direction. For consistence with much of the literature and with Northern Hemisphere observations, this is subsequently referred to as "Downshear-Left". Stronger shear magnitudes are associated with greater amplitude precipitation asymmetries. Recent work has reinforced the prior findings, and explored details of the response of the precipitation and kinematic fields to environmental vertical wind shear. Much of this research has focused on tropical cyclones away from land, to limit the influence of other processes that might distort the signal related to vertical wind shear. Recent evidence does suggest vertical wind shear can also play a major role in precipitation asymmetries during and after landfall

Potential Future NASA Satellite Data and Applications for Tropical Cyclones

No abstract availabl

Down Among The Sugar-Cane

[Verse 1] Down in Lousianna,Where the sugar can grows,Oh Oh my!Lives Miss Susiana,She\u27s the sweetest girl I know,Oh! Oh! my!When my work is over-mongst the sugar caneTo her home I go just at the bend of the lane\u27neath her cabin window I am singing this refrainOh! Oh! my! [Chorus] Can\u27t you see the night am falling?Whippoorwill am singing lowDon\u27t you hear the crickets calling?Calling you and met to goSusie don\u27t you keep me waitingIf you do \u27twill cause me painThe moon am shining, and my heart am piningMeet me down among the sugar cane [Verse 2] \u27Taint no use atalking,There will be a wedding soon,Oh, Oh my!Met her last Decemberand we are goin\u27 to wed in JuneOh, Oh my!Susiana\u27s goin\u27 to be my blushing brideI can see myself a-standing closer by her side\u27deed I\u27ll be happy person when the knot am tiedOh! Oh! my! [Chorus

A Prototype Hail Detection Algorithm and Hail Climatology Developed with the Advanced Microwave Sounding Unit (AMSU)

In previous studies published in the open literature, a strong relationship between the occurrence of hail and the microwave brightness temperatures (primarily at 37 and 85 GHz) was documented. These studies were performed with the Nimbus7 SMMR, the TRMM Microwave Imager (TMI) and most recently, the Aqua AMSRE sensor. This lead to climatologies of hail frequency from TMI and AMSRE, however, limitations include geographical domain of the TMI sensor (35 S to 35 N) and the overpass time of the Aqua satellite (130 am/pm local time), both of which reduce an accurate mapping of hail events over the global domain and the full diurnal cycle. Nonetheless, these studies presented exciting, new applications for passive microwave sensors. Since 1998, NOAA and EUMETSAT have been operating the AMSUA/B and the MHS on several operational satellites: NOAA15 through NOAA19; MetOpA and B. With multiple satellites in operation since 2000, the AMSU/MHS sensors provide near global coverage every 4 hours, thus, offering a much larger time and temporal sampling than TRMM or AMSRE. With similar observation frequencies near 30 and 85 GHz and additionally three at the 183 GHz water vapor band, the potential to detect strong convection associated with severe storms on a more comprehensive time and space scale exists. In this study, we develop a prototype AMSUbased hail detection algorithm through the use of collocated satellite and surface hail reports over the continental U.S. for a 12year period (20002011). Compared with the surface observations, the algorithm detects approximately 40 percent of hail occurrences. The simple threshold algorithm is then used to generate a hail climatology that is based on all available AMSU observations during 200011 that is stratified in several ways, including total hail occurrence by month (March through September), total annual, and over the diurnal cycle. Independent comparisons are made compared to similar data sets derived from other satellite, ground radar and surface reports. The algorithm was also applied to global land measurements for a single year and showed close agreement with other satellite based hail climatologies. Such a product could serve as a prototype for use with a future geostationary based microwave sensor such as NASA's proposed PATH mission

Tropical Cyclone Intensity Estimation Using Deep Convolutional Neural Networks

Estimating tropical cyclone intensity by just using satellite image is a challenging problem. With successful application of the Dvorak technique for more than 30 years along with some modifications and improvements, it is still used worldwide for tropical cyclone intensity estimation. A number of semi-automated techniques have been derived using the original Dvorak technique. However, these techniques suffer from subjective bias as evident from the most recent estimations on October 10, 2017 at 1500 UTC for Tropical Storm Ophelia: The Dvorak intensity estimates ranged from T2.3/33 kt (Tropical Cyclone Number 2.3/33 knots) from UW-CIMSS (University of Wisconsin-Madison - Cooperative Institute for Meteorological Satellite Studies) to T3.0/45 kt from TAFB (the National Hurricane Center's Tropical Analysis and Forecast Branch) to T4.0/65 kt from SAB (NOAA/NESDIS Satellite Analysis Branch). In this particular case, two human experts at TAFB and SAB differed by 20 knots in their Dvorak analyses, and the automated version at the University of Wisconsin was 12 knots lower than either of them. The National Hurricane Center (NHC) estimates about 10-20 percent uncertainty in its post analysis when only satellite based estimates are available. The success of the Dvorak technique proves that spatial patterns in infrared (IR) imagery strongly relate to tropical cyclone intensity. This study aims to utilize deep learning, the current state of the art in pattern recognition and image recognition, to address the need for an automated and objective tropical cyclone intensity estimation. Deep learning is a multi-layer neural network consisting of several layers of simple computational units. It learns discriminative features without relying on a human expert to identify which features are important. Our study mainly focuses on convolutional neural network (CNN), a deep learning algorithm, to develop an objective tropical cyclone intensity estimation. CNN is a supervised learning algorithm requiring a large number of training data. Since the archives of intensity data and tropical cyclone centric satellite images is openly available for use, the training data is easily created by combining the two. Results, case studies, prototypes, and advantages of this approach will be discussed

Investigating Tropical Cyclone Size and Integrated Kinetic Energy using CYGNSS and Other Datasets

No abstract availabl

Hurricane Karl's Structure and Some Thoughts for 2014 Strategy

No abstract availabl

Tropical Cyclone Intensity Estimation Using Deep Convolutional Neural Networks

Overview: Deep learning and Convolutional Neural Network (CNN); CNN for Tropical Cyclone Intensity Estimation; Preliminary results; Work in progress

The ENSO Effect on the Temporal and Spatial Distribution of Global Lightning Activity

The recently reprocessed (1997-2006) OTD/LIS database is used to investigate the global lightning climatology in response to the ENSO cycle. A linear correlation map between lightning anomalies and ENSO (NINO3.4) identifies areas that generally follow patterns similar to precipitation anomalies. We also observed areas where significant lightning/ENSO correlations are found and are not accompanied of significant precipitation/ENSO correlations. An extreme case of the strong decoupling between lightning and precipitation is observed over the Indonesian peninsula (Sumatra) where positive lightning/NINO3.4 correlations are collocated with negative precipitation/NINO3.4 correlations. Evidence of linear relationships between the spatial extent of thunderstorm distribution and the respective NINO3.4 magnitude are presented for different regions on the Earth. Strong coupling is found over areas remote to the main ENSO axis of influence and both during warm and cold ENSO phases. Most of the resulted relationships agree with the tendencies of precipitation related to ENSO empirical maps or documented teleconnection patterns. Over the Australian continent, opposite behavior in terms of thunderstorm activity is noted for warm ENSO phases with NINO3.4 magnitudes with NINO3.4>+l.08 and 0<NqNO3.4<I.08. Finally, we investigate the spatial distribution of areas that consistently portrayed enhanced lightning activity during the main warm/cold (El Nino/La Nina) ENSO episodes of the past decade. The observed patterns show no spatial overlapping and identify areas that in their majority are in agreement with empirical precipitation/ENSO maps. The areas that appear during the warm ENSO phase are found over regions that have been identified as anomalous Hadley circulation ENSO-related patterns. The areas that appear during the cold ENSO phase are found predominantly around the west hemisphere equatorial belt and are in their majority identified by anomalous Walker circulation