scholarly journals Diagnosing Santa Ana Winds in Southern California with Synoptic-Scale Analysis

2013 ◽  
Vol 28 (3) ◽  
pp. 704-710 ◽  
Author(s):  
John T. Abatzoglou ◽  
Renaud Barbero ◽  
Nicholas J. Nauslar
Keyword(s):  
Sea Level ◽  
Southern California ◽  
The United States ◽  
Fire Weather ◽  
Synoptic Scale ◽  
Sea Level Pressure ◽  
Mean Sea Level ◽  
Santa Ana ◽  
Level Pressure ◽  
Santa Ana Winds

Abstract Santa Ana winds (SAW) are among the most notorious fire-weather conditions in the United States and are implicated in wildfire and wind hazards in Southern California. This study employs large-scale reanalysis data to diagnose SAW through synoptic-scale dynamic and thermodynamic factors using mean sea level pressure gradient and lower-tropospheric temperature advection, respectively. A two-parameter threshold model of these factors exhibits skill in identifying surface-based characteristics of SAW featuring strong offshore winds and extreme fire weather as viewed through the Fosberg fire weather index across Remote Automated Weather Stations in southwestern California. These results suggest that a strong northeastward gradient in mean sea level pressure aligned with strong cold-air advection in the lower troposphere provide a simple, yet effective, means of diagnosing SAW from synoptic-scale reanalysis. This objective method may be useful for medium- to extended-range forecasting when mesoscale model output may not be available, as well as being readily applied retrospectively to better understand connections between SAW and wildfires in Southern California.

scholarly journals Brief Communication: Synoptic-scale differences between Sundowner and Santa Ana wind regimes in the Santa Ynez Mountains, California

2018 ◽  
Vol 18 (2) ◽  
pp. 419-427 ◽  
Author(s):  
Benjamin J. Hatchett ◽  
Craig M. Smith ◽  
Nicholas J. Nauslar ◽  
Michael L. Kaplan
Keyword(s):  
Sea Level ◽  
Western North America ◽  
Synoptic Scale ◽  
Sea Level Pressure ◽  
Mean Sea Level ◽  
Santa Ana ◽  
Coastal Jet ◽  
Santa Ana Winds ◽  
Wind Regimes ◽  
Surface Observations

Abstract. Downslope Sundowner winds in southern California's Santa Ynez Mountains favor wildfire growth. To explore differences between Sundowners and Santa Ana winds (SAWs), we use surface observations from 1979 to 2014 to develop a climatology of extreme Sundowner days. The climatology was compared to an existing SAW index from 1979 to 2012. Sundowner (SAW) occurrence peaks in late spring (winter). SAWs demonstrate amplified 500 hPa geopotential heights over western North America and anomalous positive inland mean sea-level pressures. Sundowner-only conditions display zonal 500 hPa flow and negative inland sea-level pressure anomalies. A low-level northerly coastal jet is present during Sundowners but not SAWs.

scholarly journals Brief Communication: Differences between Sundowner and Santa Ana wind regimes in the Santa Ynez Mountains, California

2017 ◽  
Author(s):  
Benjamin J. Hatchett ◽  
Craig M. Smith ◽  
Nicholas J. Nauslar ◽  
Michael L. Kaplan
Keyword(s):  
North America ◽  
Sea Level ◽  
Western North America ◽  
Sea Level Pressure ◽  
Mean Sea Level ◽  
Santa Ana ◽  
Santa Ana Winds ◽  
Communication Differences ◽  
Wind Regimes ◽  
Surface Observations

Abstract. Strong afternoon downslope Sundowner winds in southern California's Santa Ynez Mountains favor wildfire growth. To determine whether Sundowners are different from Santa Ana winds (SAW), we use surface observations from 1979–2014 to develop a climatology of extreme Sundowner days. The climatology was compared against an existing SAW index from 1979–2012. Sundowner occurrence peaks in late spring whereas SAWs peak during winter. SAWs demonstrate amplified 500 hPa geopotential heights over western North America and strong anomalously positive inland mean sea level pressures. In contrast, Sundowner-only conditions occur during zonal 500 hPa flow and moderate negative inland sea level pressure anomalies.

scholarly journals Tropical cyclone simulations over Bangladesh at convection permitting 4.4 km & 1.5 km resolution

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Hamish Steptoe ◽  
Nicholas Henry Savage ◽  
Saeed Sadri ◽  
Kate Salmon ◽  
Zubair Maalick ◽  
...  
Keyword(s):  
Wind Speed ◽  
Tropical Cyclone ◽  
Sea Level ◽  
Tropical Cyclones ◽  
Weather Forecasting ◽  
Sea Level Pressure ◽  
Mean Sea Level ◽  
Level Pressure ◽  
Medium Range ◽  
Gust Speed

AbstractHigh resolution simulations at 4.4 km and 1.5 km resolution have been performed for 12 historical tropical cyclones impacting Bangladesh. We use the European Centre for Medium-Range Weather Forecasting 5th generation Re-Analysis (ERA5) to provide a 9-member ensemble of initial and boundary conditions for the regional configuration of the Met Office Unified Model. The simulations are compared to the original ERA5 data and the International Best Track Archive for Climate Stewardship (IBTrACS) tropical cyclone database for wind speed, gust speed and mean sea-level pressure. The 4.4 km simulations show a typical increase in peak gust speed of 41 to 118 knots relative to ERA5, and a deepening of minimum mean sea-level pressure of up to −27 hPa, relative to ERA5 and IBTrACS data. The downscaled simulations compare more favourably with IBTrACS data than the ERA5 data suggesting tropical cyclone hazards in the ERA5 deterministic output may be underestimated. The dataset is freely available from 10.5281/zenodo.3600201.

scholarly journals Prediction of synoptic-scale sea level pressure over the Indian monsoon region using deep learning

2021 ◽  
Author(s):  
Aryaman Sinha ◽  
Mayuna Gupta ◽  
K S S Sai Srujan ◽  
Hariprasad Kodamana ◽  
Sandeep Sukumaran
Keyword(s):  
Deep Learning ◽  
Sea Level ◽  
Short Term Memory ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Learning Model ◽  
Synoptic Scale ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
Deep Learning Model

<div><div><div><p>The synoptic-scale (3 - 7 days) variability is a dominant contributor to the Indian summer monsoon (ISM) seasonal precipitation. An accurate prediction of ISM precipitation by dynamical or statistical models remains a challenge. Here we show that the sea level pressure (SLP) can be used as a proxy to predict the active-break cycle as well as the genesis of low- pressure-systems (LPS), using a deep learning model, namely, convolutional long short-term memory (ConvLSTM) networks. The deep learning model is able to reliably predict the daily SLP anomalies over Central India and the Bay of Bengal at a lead time of 7 days. As the fluctuations in SLP drive the changes in the strength of the atmospheric circulation, the prediction of SLP anomalies is useful in predicting the intensity of ISM. It is demonstrated that the ConvLSTM possesses better prediction skill compared to a conventional numerical weather prediction model, indicating the usefulness of a physics guided deep learning model in medium range weather forecasting.</p></div></div></div>

A Weather Singularity over the U. S. in October

1954 ◽  
Vol 35 (8) ◽  
pp. 351-356 ◽  
Author(s):  
Eberhard W. Wahl
Keyword(s):  
United States ◽  
Sea Level ◽  
The United States ◽  
Sudden Increase ◽  
Sea Level Pressure ◽  
Pressure Pattern ◽  
Level Pressure

A sudden increase in the probability of snow occurrence during the month of October at Denver, Colorado, had been reported. It is shown that this increase can be associated with the development of a widespread weather singularity occurring at that time of the year. The normal sea-level pressure-pattern changes derived from 40 years of data over the United States show the synoptic development of this singularity. This development leads to peculiarities in various weather elements at that time.

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