SAMURAI analysis of dynamic and thermodynamic variables and convective frequency in 20 African easterly waves using satellite brightness temperatures
Abstract
This dataset includes analysis fields of various thermodynamic and dynamical variables utilizing the Spline Analysis at Mesoscale Utilizing Radar and Aircraft Instrumentation (SAMURAI) of 20 selected cases of African easterly waves (AEWs), associated with research flights during the NASA African Monsoon Multidisciplinary Analyses (NAMMA) in 2006 and the Convective Processes Experiment - Cabo Verde (CPEX-CV) in 2022. Each SAMURAI analysis uses ERA5 at 1200 UTC as the background field and incorporates aircraft observations from the NASA DC-8 by performing a time-space correction due to the movement of the wave. Observations from dropsondes, flight level instruments, the High-Altitude Monolithic Microwave Integrated Circuit (MMIC) Sounding Radiometer (HAMSR), and the Doppler aerosol wind (DAWN) lidar were incorporated for the 2022 cases, and just dropsondes and flight level data for the 2006 cases. Each SAMURAI analysis grid is centered on a PV centroid (point that maximizes the area integrated potential vorticity in the lower troposphere), is 1000 x 1000 km (± 500 km from the center) with a 10 km horizontal grid spacing and 1 km vertical grid spacing. An .nc file is provided for each case, including thermodynamic and dynamical variables provided by SAMURAI, except for potential vorticity (PV), which was calculated using a metpy function. The frequency of occurrence of clear air (Prob_clear), shallow/moderate (Prob_sha), and deep (Prob_deep) convection over 6 hours (9000 UTC to 15 UTC) for each of the 20 cases was calculated and provided as an .nc file. The spatial frequency of each classification type was calculated for each case by counting the grid points that had each type of convection across the images over the 6 hours and dividing by the total number of images (12 for 2006 cases and 36 for 2022 cases). The fields match the SAMURAI analysis center, grid spacing, and horizontal and vertical boundaries for each case. This dataset can be used to assess the thermodynamic and dynamical structure of AEWs and their relationship to convective organization.
Dataset DOI: 10.5061/dryad.jh9w0vtq2
Description of the data and file structure
SAMURAI Analysis of dynamic and thermodynamic variables and frequency of convection types calculated from satellite brightness temperatures, of 20 African Easterly Wave cases from the NAMMA and CPEX-CV field projects.
Files and variables
File: data.zip
SAMURAI analysis vortex centric fields (20 .nc files)
Description:
We employ the Spline Analysis at Mesoscale Utilizing Radar and Aircraft Instrumentation (SAMURAI)^ 1,2 ^ to integrate NASA DC-8 observations with reanalysis data. The SAMURAI analysis incorporates NASA DC8 aircraft observations from dropsondes, flight level data, the High-Altitude Monolithic Microwave Integrated Circuit (MMIC) Sounding Radiometer (HAMSR), and the Doppler aerosol wind (DAWN) lidar onto a first guess field (ERA5)3at 1200 UTC for 20 selected cases of African easterly waves (AEWs), associated with research flights during the NASA African Monsoon Multidisciplinary Analyses (NAMMA) in 2006 and the Convective Processes Experiment- Cabo Verde (CPEX-CV).
- Dimensions: longitude (101), latitude (101), time (1), altitude (19)
- Horizontal spacing: 10 km
- Vertical spacing: 1 km vertical
| Variable | Long name | Units |
|---|---|---|
| X | longitude | km |
| Y | (latitude) | km |
| U | zonal wind component) | m s -1 |
| V | Meridional wind component | m s -1 |
| W | vertical wind component | m s -1 |
| WSPD | wind speed | m s -1 |
| RH | relative humidity | % |
| THETA | potential temperature | K |
| P | pressure | hPa |
| PP | pressure perturbation | hPa |
| VORT | vertical vorticity + | 10 -5 s -1 |
| ABSVORT | absolute vertical vorticity + | 10 -5 s -1 |
| DIV | divergence | 10 -5 s -1 |
| T | temperature | K |
| PV | potential vorticity *+ | K m2 kg -1 s -1 |
+ with a Gaussian filter applied
*calculated variable using metpy 4 potential vorticity equation 5
Frequency of convection from satellite brightness temperatures (1 file): frequency_convec.nc
Description:
The frequency of occurrence of clear air, shallow/moderate, and deep convection over 6 hours (9000 UTC to 15 UTC) was calculated for each AEW case based on satellite brightness temperature images from the Geostationary Operational Environmental Satellite (GOES16)6. The spatial frequency of each classification type was calculated by counting the grid points that had each type of convection across the images over the 6 hours and dividing by the total number of images (12 for 2006 cases and 36 for 2022 cases). The images used had a domain of 1000 x 1000 km, centered on a PV centroid, which accounted for the wave motion during the 6 hours. See Table 2 in Colón-Burgos and Bell, 2025 (submitted) for the temperature thresholds used to perform the convective classifications.
- Dimensions: time (20), x (101), y (101)
- Horizontal spacing: 10 km
| Variable | Long name | Units |
|---|---|---|
| Prob_deep | probability deep convection | na |
| Prob_sha | probability shallow/moderate convection | na |
| Prob_clear | probability of clear air | na |
Access information
References
1. Bell, M. M., Montgomery, M. T., & Emanuel, K. A. (2012). Air–Sea Enthalpy and Momentum Exchange at Major Hurricane Wind Speeds Observed during CBLAST. https://doi.org/10.1175/JAS-D-11-0276.1
2. Cha, T.-Y., & Bell, M. M. (2023). Three-Dimensional Variational Multi-Doppler Wind Retrieval over Complex Terrain. https://doi.org/10.1175/JTECH-D-23-0019.1
3. Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J.-N. (2023): ERA5 hourly data on pressure levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.bd0915c6
4. May, R. M., Goebbert, K. H., Thielen, J. E., Leeman, J. R., Camron, M. D., Bruick, Z., Bruning, E. C., Manser, R. P., Arms, S. C., and Marsh, P. T., 2022: MetPy: A Meteorological Python Library for Data Analysis and Visualization. Bull. Amer. Meteor. Soc., 103, E2273-E2284, https://doi.org/10.1175/BAMS-D-21-0125.1.
5. Bluestein, H. B., 1993: Observations and Theory of Weather Systems. Vol. 2, Synoptic-Dynamic Meteorology in Midlatitudes. Oxford University Press, 608 pp.
6. GOES-R Calibration Working Group and GOES-R Series Program (2017): NOAA GOES-R Series Advanced Baseline Imager (ABI) Level 1b Radiances. [CMI_C13]. NOAA National Centers for Environmental Information. doi:10.7289/V5BV7DSR. [June 2024].