Atmosphere

Discussion goes here

Masayoshi ISHII, Hirotaka KAMAHORI, Hisayuki KUBOTA, Masumi ZAIKI, Ryo MIZUTA, Hiroaki KAWASE, Masaya NOSAKA, Hiromasa YOSHIMURA, Naga OSHIMA, Eiki SHINDO, Hiroshi KOYAMA, Masato MORI, Shoji HIRAHARA, Yukiko IMADA, Kohei YOSHIDA, Toru NOZAWA, Tetsuya TAKEMI, Takashi MAKI, Akio NISHIMURA, Global Historical Reanalysis with a 60-km AGCM and Surface Pressure Observations: OCADA, Journal of the Meteorological Society of Japan. Ser. II, 2024, Volume 102, Issue 2, Pages 209-240, Released on J-STAGE March 09, 2024, Advance online publication January 17, 2024, Online ISSN 2186-9057, Print ISSN 0026-1165, https://doi.org/10.2151/jmsj.2024-010, https://www.jstage.jst.go.jp/article/jmsj/102/2/102_2024-010/_article/-char/en, 

Abstract:  

A historical atmospheric reanalysis from 1850 to 2015 was performed using an atmospheric general circulation model assimilating surface pressure observations archived in international databases, with perturbed observational sea surface temperatures as a lower boundary condition. Posterior spread during data assimilation provides quantitative information on the uncertainty in the historical reanalysis. The reanalysis reproduces the evolution of the three-dimensional atmosphere close to those of the operational centers. Newly archived surface pressure observations greatly reduced the uncertainties in the present reanalysis over East Asia in the early 20th century. A scheme for assimilating tropical cyclone tracks and intensities was developed. The scheme was superior to the present several reanalyses in reproducing the intensity close to the observations and the positions. The reanalysis provides possible images of atmospheric circulations before reanalyses with full-scale observations become available, and opportunities for investigating extreme events that occurred before World War II. Incorporating dynamical downscaling with a regional model that includes detailed topography and sophisticated physics is an application of historical reanalysis to reveal the details of past extreme events. Some examples of past heavy rainfall events in Japan are shown using a downscaling experiment, together with dense rainfall observations over the Japanese islands.

A historical atmospheric reanalysis from 1850 to 2015 was performed using an atmospheric general circulation model assimilating surface pressure observations archived in international databases, with perturbed observational sea surface temperatures as a lower boundary condition. Posterior spread during data assimilation provides quantitative information on the uncertainty in the historical reanalysis. The reanalysis reproduces the evolution of the three-dimensional atmosphere close to those of the operational centers. Newly archived surface pressure observations greatly reduced the uncertainties in the present reanalysis over East Asia in the early 20th century. A scheme for assimilating tropical cyclone tracks and intensities was developed. The scheme was superior to the present several reanalyses in reproducing the intensity close to the observations and the positions. The reanalysis provides possible images of atmospheric circulations before reanalyses with full-scale observations become available, and opportunities for investigating extreme events that occurred before World War II. Incorporating dynamical downscaling with a regional model that includes detailed topography and sophisticated physics is an application of historical reanalysis to reveal the details of past extreme events. Some examples of past heavy rainfall events in Japan are shown using a downscaling experiment, together with dense rainfall observations over the Japanese islands.

An incomplete list can be found here:

https://www.cpc.ncep.noaa.gov/products/wesley/CORe/references.html

There are a number of variables of potential interest to researches investigating links between weather/climate and infectious diseases. The most common ones 2m temperature, 2m relative humidity, 2m specific humidity, and precipitation. There are other potential variables including 2m Tmax and Tmin. Most reanalyses make data available at monthly and daily (or sub daily time scales).

Here are ways to get these variables from different reanalysis is not an specific endorsement for this research. Most values will have an "error bar". Researchers should carefully read documentation on limitations and consider other sources as well. 

20CRv3 1836-2015. Uses pressure as input. 

ftp ftp.cdc.noaa.gov

cd to 

2m temperature: /Datasets/20thC_ReanV3/Dailies/2mMO/air.2m.yyyy.nc (for previous 1981 use 2mSI).

                           /Datasets/20thC_ReanV3/Monthlies/2mSI-MO/air.2m.mon.mean.nc

2m specific humidity:  /Datasets/20thC_ReanV3/Dailies/2mMO/shum.2m.yyyy.nc (for presvios1981 use 2mSI).

                          /Datasets/20thC_ReanV3/Monthlies/2mSI-MO/shum2m.mon.mean.nc

2m relative humidity:  /Datasets/20thC_ReanV3/Dailies/2mMO/rhum2m.yyyy.nc (for previous 1981 use 2mSI).

                         /Datasets/20thC_ReanV3/Monthlies/2mSI-MO/rhum.2m.mon.mean.nc

precipitation rate: :  /Datasets/20thC_ReanV3/Dailies/sfcMO/air.2m.yyyy.nc (for previous 1981 use sfcSI).

                         /Datasets/20thC_ReanV3/Monthlies/sfcSI-MO/air.2m.mon.mean.nc

JRA-55: 1958-present

Get a login

ncftp -u username -p password ds.data.jma.go.jp

cd /JRA-55/Hist/Monthly/

download files: go to directory/

2mT: anl_surf125: var name TMP_GDS0_HTGL_S113

rhum:anl_surf125: R_GDS0_ISBL_S113"

2mq anl_surf125: var name PFH_GDS0_HTGL_S113

prate fcst_phy2m125.TPRAT_GDS0_SFC_S130

Files are grib Convert to netCDF

ERA20-C: 1900-2010?

2m temperature: Month

2m specific humidity

2m relative humidity

precipitation rate: 

NCEP Reanalysis 1948-2020.

 Updated to near present and starts earlier than most.  Lower resolution than some. Usees an older model. 

ftp ftp.cdc.noaa.gov

cd to 

2m temperature: /Datasets/ncep.reanalysis.dailyavgs/air.2m.yyyy.nc (day) /Datasets/ncep.reanalysis.derived/air.2m.mon.mean.nc (month)

2m specific humidity: /Datasets/ncep.reanalysis.dailyavgs/air.2m.yyyy.nc (day) /Datasets/ncep.reanalysis.derived/air.2m.mon.mean.nc (month)

2m relative humidity: /Datasets/ncep.reanalysis.dailyavgs/air.2m.yyyy.nc (day) /Datasets/ncep.reanalysis.derived/air.2m.mon.mean.nc (month)

precipitation rate:: /Datasets/ncep.reanalysis.dailyavgs/air.2m.yyyy.nc (day) /Datasets/ncep.reanalysis.derived/air.2m.mon.mean.nc (month)

ERA-5

Monthly

Not sure how to get daily.,

ncep-notes-questions-and-discussion

Current reference

The ERA5 Global Reanalysis  Hersbach, H. et al. May 2020. QJRMS

please cite the article as doi: 10.1002/qj.3803.

Other References

ECMWF Library

How to cite

Dataset citable as: Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate . Copernicus Climate Change Service Climate Data Store (CDS), date of access. https://cds.climate.copernicus.eu/cdsapp#!/home

MERRA-2 references

First pick the correct variable here:

https://disc.gsfc.nasa.gov/datasets?page=1&project=MERRA-2&keywords=%22MERRA-2%22

When you click on the correct variable, it will take you to a second webpage with tabs that you can click that include: (1) documentation papers you need to cite, and (2) the correct variable citation information. (And you apparently need both types of citation). For example, below is a link to the page for 3d assimilated, 3-hourly, model level data (i.e. the equivalent of what everyone uses for 6-hourly ERA-Interim variables like temperature).

https://disc.gsfc.nasa.gov/datasets/M2I3NVASM_V5.12.4/summary?keywords=%22MERRA-2%22