Abstract This study compares the spread in climatological tropical cyclone (TC) precipitation across eight different reanalysis datasets: NCEP-CFSR, ERA-20C, ERA-40, ERA5, ERA-Interim, JRA-55, MERRA-2, and NOAA-20C. TC precipitation is assigned using manual tracking via a fixed 500-km radius from each TC center. The reanalyses capture similar general spatial patterns of TC precipitation and TC precipitation fraction, defined as the fraction of annual precipitation assigned to TCs, and the spread in TC precipitation is larger than the spread in total precipitation across reanalyses. The spread in TC precipitation relative to the inter-reanalysis mean TC precipitation, or relative spread, is larger in the east Pacific than in the west Pacific. Partitioned by reanalysis intensity, the largest relative spread across reanalyses in TC precipitation is from high-intensity TCs. In comparison with satellite observations, reanalyses show lower climatological mean annual TC precipitation over most areas. A comparison of area-averaged precipitation rate in TCs composited over reanalysis intensity shows the spread across reanalyses is larger for higher intensity TCs. Testing the sensitivity of TC precipitation assignment to tracking method shows that climatological mean annual TC precipitation is systematically larger when assigned via manual tracking versus objective tracking. However, this tendency is minimized when TC precipitation is normalized by TC density. Overall, TC precipitation in reanalyses is affected by not only horizontal output resolution or any TC preprocessing, but also data assimilation and parameterization schemes. The results indicate that improvements in the representation of TCs and their precipitation in reanalyses are needed to improve overall precipitation.
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Tropical Cyclones in the NASA GEOS-5 Model and MERRA-2 Reanalysis
Studies of projected changes in tropical cyclones under anthropogenic climate change, as well as their modulation by internal climate modes, make use of global climate models. To this end, tropical cyclones can be tracked in the output of higher resolution models. Using climate models to make future projections of tropical cyclones relies upon having a baseline of the characteristics of model storms under the current climate. This study focuses on two high-resolution datasets – the NASA GEOS-5 Model (Goddard Earth Observing System Model, Version 5) and the MERRA-2 Reanalysis (Modern-Era Retrospective analysis for Research and Applications, Version 2). Both of these datasets were created using exactly the same atmospheric model during the same period. However, while GEOS-5 is a free-running atmospheric model forced only with sea surface temperature, MERRA-2 is a reanalysis product, i.e. the model assimilates data from a large variety of data sources. Thus, by comparing tropical cyclones tracked in these datasets to each other and global best track datasets in the period 1980-1999, this project aims to evaluate 1) the sensitivity of this model to how it is forced and 2) how well the storms tracked in GEOS-5 and MERRA-2 replicate observed tropical cyclones’ characteristics. We used two different tracking schemes on both datasets and found no significant difference in the performance of the model and the reanalysis in simulating tropical cyclones. Standard diagnostics for tropical cyclones, such as the mean number, intensity distribution, as well as their interannual variability are very similar in the free-running model and the reanalysis. Both GEOS-5 and MERRA-2 show a bias towards weaker tropical cyclones than observed and GEOS-5 has storms that occur closer to the equator than in the observed record. Neither GEOS-5 nor MERRA-2 accurately reproduce tropical cyclone modulation by ENSO. Additionally, comparison of MERRA-2 to other reanalysis datasets shows that MERRA-2 on average generates fewer total but also more intense storms than the European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim) and Japanese 55-Year Reanalysis (JRA-55). Further research must be performed to understand why this data assimilation is failing to provide a positive impact on the tropical cyclone simulation in this model.
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- Award ID(s):
- 1757602
- PAR ID:
- 10091135
- Date Published:
- Journal Name:
- American Meteorological Society Annual Meeting, Abstract S251
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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{"Abstract":["This is software and data to support the manuscript "Variations in Tropical Cyclone Size and Rainfall Patterns based on Synoptic-Scale Moisture Environments in the North Atlantic," which we are submitting to the journal, Journal of Geophysical Research Atmospheres.The MIT license applies to all source code and scripts published in this dataset.The software includes all code that is necessary to follow and evaluate the work. Public datasets include (1) the Atlantic hurricane database HURDAT2 (https://www.nhc.noaa.gov/data/#hurdat), (2) NASA’s Global Precipitation Measurement IMERG final precipitation (https://catalog.data.gov/dataset/gpm-imerg-final-precipitation-l3-half-hourly-0-1-degree-x-0-1-degree-v07-gpm-3imerghh-at-g), (3) the Tropical Cyclone Extended Best Track Dataset (https://rammb2.cira.colostate.edu/research/tropical-cyclones/tc_extended_best_track_dataset/), (4) the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5) (https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5), and (5) the Statistical Hurricane Intensity Prediction Scheme (SHIPS) dataset (https://rammb.cira.colostate.edu/research/tropical_cyclones/ships/data/). We are also including four datasets generated by the code that will be helpful in evaluating the work. Lastly, we used the eofs software package, a python package for computing empirical orthogonal functions (EOFs), available publicly here: https://doi.org/10.5334/jors.122.All figures and tables in the manuscript are generated using Python, ArcGIS Pro, and GraphPad/Prism 10 Software:ArcGIS Pro used to make Figures 5GraphPad/Prism 10 Software used to make box plots in Figures 6-9Python used to make Figures 1-4, 10-11, and Tables 1-5Public Datasets:HURDAT2: Landsea, C. and Beven, J., 2019: The revised Atlantic hurricane database (HURDAT2). March 2022, https://www.aoml.noaa.gov/hrd/hurdat/hurdat2-format.pdfIMERG:NASA EarthData: GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V06. 9 December 2024, https://catalog.data.gov/dataset/gpm-imerg-final-precipitation-l3-half-hourly-0-1-degree-x-0-1-degree-v07-gpm-3imerghh-at-g. Note that this dataset is not longer publicly available, as it has been replaced with IMERG version 7: https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGHH_07/summary?keywords="IMERG final"Extended Best Track:Regional and Mesoscale Meteorology Branch, 2022: The Tropical Cyclone Extended Best Track Dataset (EBTRK). March 2022, https://rammb2.cira.colostate.edu/research/tropical-cyclones/tc_extended_best_track_dataset/ERA5: Guillory, A. (2022). ERA5. Ecmwf [Dataset]. https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5. (Accessed March 2, 2023). Also: Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146, 1999–2049, https://doi.org/10.1002/qj.3803SHIPS:Ships Predictor Files - Colorado State University (2022). Statistical Tropical Cyclone Intensity Forecast Technique Development. https://rammb.cira.colostate.edu/research/tropical_cyclones/ships/data/ships_predictor_file_2022.pdf. Also: DeMaria, M., and J. Kaplan, 1994: A Statistical Hurricane Intensity Prediction Scheme (SHIPS) for the Atlantic Basin. Weather and Forecasting, 9, 209–220, https://doi.org/10.1175/1520-0434(1994)009<0209:ASHIPS>2.0.CO;2.Public Software: Dawson, A., 2016: eofs: A Library for EOF Analysis of Meteorological, Oceanographic, and Climate Data. JORS, 4, 14, https://doi.org/10.5334/jors.122.van der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., et al. (2014). Scikit-image: Image processing in Python [Software]. PeerJ, 2, e453. https://doi.org/10.7717/peerj.453"]}more » « less
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