tsgettoolbox.tsgettoolbox.ldas_nldas_fora

tsgettoolbox.tsgettoolbox.ldas_nldas_fora(lat=None, lon=None, variables=None, startDate=None, endDate=None, variable=None)

NAmerica:0.125deg:1979-:H:NLDAS Weather Forcing A (surface)

The time zone is always UTC.

The non-precipitation land-surface forcing fields for NLDAS-2 are derived from the analysis fields of the NCEP North American Regional Reanalysis (NARR). NARR consists of: 1) a retrospective dataset starting from Jan 1979, and 2) a daily update execution at NCEP. The daily update provides a real-time NARR continuation known as the Regional Climate Data Assimilation System, or R-CDAS.

NARR analysis fields are 32-km spatial resolution and 3-hourly temporal frequency. Those NARR fields that are utilized to generate NLDAS-2 forcing fields are spatially interpolated to the finer resolution of the NLDAS 1/8th-degree grid and then temporally disaggregated to the NLDAS-2 hourly frequency. Additionally, the fields of surface pressure, surface downward longwave radiation, near-surface air temperature and near-surface specific humidity are adjusted vertically to account for the vertical difference between the NARR and NLDAS fields of terrain height. This vertical adjustment applies the traditional vertical lapse rate of 6.5 K/km for air temperature. The details of the spatial interpolation, temporal disaggregation, and vertical adjustment are those employed in NLDAS-1, as presented by Cosgrove et al. (2003).

The hourly land-surface forcing fields for NLDAS-2 are grouped into two GRIB files, “File A” and “File B”. This is a change from NLDAS-1, which had only one hourly forcing file.

File A contains data for surface, 2 meter, and 10 meter heights. File B contains data for the lowest layer of the model used for data assimilation.

The surface downward shortwave radiation field in File A is a bias-corrected field wherein a bias-correction algorithm was applied to the NARR surface downward shortwave radiation. This bias correction utilizes five years (1996-2000) of the hourly 1/8th-degree GOES-based surface downward shortwave radiation fields derived by Pinker et al. (2003).

The precipitation field in File A is not the NARR precipitation forcing, but is rather a product of a temporal disaggregation of a gauge-only CPC analysis of daily precipitation, performed directly on the NLDAS grid and including an orographic adjustment based on the widely-applied PRISM climatology.

The field in File A that gives the fraction of total precipitation that is convective is an estimate derived from the following two NARR precipitation fields (which are provided in File B): NARR total precipitation and NARR convective precipitation (the latter is less than or equal to the NARR total precipitation and can be zero). The convective fraction of total precipitation and/or the CAPE field in File A are used by some land models to estimate the subgrid spatial variability of the total precipitation.

The potential evaporation field in File A is that computed in NARR using the modified Penman scheme of Mahrt and Ek (1984). Potential evaporation is needed by some land models (such as the SAC model) that require potential evaporation as an input forcing.

Baldwin, M., and K.E. Mitchell, 1997: The NCEP hourly multi-sensor U.S. precipitation analysis for operations and GCIP research. Preprints, 13th AMS Conference on Hydrology, pp. 54-55, Am. Meteorol. Soc., Boston, Mass.

Berg, A.A., J.S. Famiglietti, J.P. Walker, and P.R. Houser, 2003: Impact of bias correction to reanalysis products on simulations of North American soil moisture and hydrological fluxes. J. Geophys. Res., 108(D16), 4490, doi:10.1029/2002JD003334.

Cosgrove, B.A., et al., 2003: Real-time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project. J. Geophys. Res., 108(D22), 8842, doi:10.1029/2002JD003118.

Daly, C., R.P. Neilson, and D.L. Phillips, 1994: A statistical-topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteor., 33, 140-158, doi:10.1175/1520-0450(1994)033<0140:ASTMFM>2.0.CO;2

Fulton, R.A., J.P. Breidenbach, D.J. Seo, D.A. Miller, and T. O’Bannon, 1998: The WSR-88D rainfall algorithm. Weather and Forecasting, 13, 377-395.

Higgins, R.W., J.E. Janowiak and Y. Yao, 1996: A gridded hourly precipitation data base for the United States (1963-1993). NCEP/Climate Prediction Center Atlas No. 1.

Higgins, R.W., W. Shi, E. Yarosh, and R. Joyce, 2000: Improved United States precipitation quality control system and analysis. NCEP/Climate Prediction Center Atlas No. 7.

Mitchell, K.E., et al., 2004: The multi-institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system. J. Geophys. Res., 109, D07S90, doi:10.1029/2003JD003823.

Mo, K.C., L.-C. Chen, S. Shukla, T.J. Bohn, and D.P. Lettenmaier, 2012: Uncertainties in North American Land Data Assimilation Systems over the Contiguous United States. J. Hydrometeor, 13, 996-1009, doi:10.1175/JHM-D-11-0132.1

Pinker, R.T., et al., 2003: Surface radiation budgets in support of the GEWEX Continental-Scale International Project (GCIP) and the GEWEX Americas Prediction Project (GAPP), including the North American Land Data Assimilation System (NLDAS) project. J. Geophys. Res., 108(D22), 8844, doi:10.1029/2002JD003301.

Description/Name

Spatial

Lat Range

Lon Range

Time

NLDAS Primary Forcing Data NLDAS_FORA0125_H V2.0

0.125x0.125

25, 53

-125, -67

1 hour 1979-01-01T13 till recent
Parameters:

  • lat (float) – Latitude (required): Enter single geographic latitude point. Use positive values for the northern hemisphere and negative for the southern hemisphere. The valid range is specified in the table above.

  • lon (float) – Longitude (required): Enter single geographic longitude point. Use positive for the eastern hemisphere and negative for the western hemisphere. The valid range is specified in the table above.

  • variables (str) –

    For the command line a comma separated string of variable codes from the following table. Using the Python API a list of variable strings. Valid variable names are specified in the table below.

    LDAS “variables” string

    Description

    Units

    NLDAS_FORA0125_H_2_0_CAPE

    Convective Available Potential Energy

    J/kg

    NLDAS_FORA0125_H_2_0_CRainf_frac

    Fraction of total precipitation that is convective

    fraction

    NLDAS_FORA0125_H_2_0_LWdown

    Surface DW longwave radiation flux

    W/m**2

    NLDAS_FORA0125_H_2_0_PotEvap

    Potential evaporation

    mm

    NLDAS_FORA0125_H_2_0_PSurf

    Surface pressure

    Pa

    NLDAS_FORA0125_H_2_0_Qair

    2-m above ground specific humidity

    kg/kg

    NLDAS_FORA0125_H_2_0_Rainf

    Precipitation hourly total

    mm

    NLDAS_FORA0125_H_2_0_SWdown

    Surface DW shortwave radiation flux

    W/m**2

    NLDAS_FORA0125_H_2_0_Tair

    2-m above ground temperature

    K

    NLDAS_FORA0125_H_2_0_Wind_E

    10-m above ground zonal wind

    m/s

    NLDAS_FORA0125_H_2_0_Wind_N

    10-m above ground meridional wind

    m/s

  • startDate (str) –

    The start date of the time series.:

    Example: --startDate=2001-01-01T05

    If startDate and endDate are None, returns the entire series.

  • endDate (str) –

    The end date of the time series.:

    Example: --endDate=2002-01-05T05

    If startDate and endDate are None, returns the entire series.

  • variable (str) – DEPRECATED: use “variables” instead to be consistent across “tsgettoolbox”.