Initialization with the data assimilation method

Miyakoda, K.; Strickler, R. F.; Chludzinski, J.

doi:10.3402/tellusa.v30i1.10312

Cited by 5 publications

(1 citation statement)

References 43 publications

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“…More recent experiments with the GFDL model using conventional NMC initial conditions [Miyakoda et al, 1978;Sirutis et al, 1980] show that it takes 2.5 to 3 days of model integration for the tropical rainfall to "spin up," i.e., to reach normal intensity. This experiment included convective precipitation; the Smagorinsky et al [1970] experiment did not [Lejeniis, 1979].…”

Section: Random Instrumental Errors Of 3 M S-• In Each Wind Compo-mentioning

confidence: 99%

Satellite remote sensing of meteorological parameters for global numerical weather prediction

Isaacs¹,

Hoffman²,

Kaplan³

1986

Reviews of Geophysics

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Remote sensing of meteorological parameters helps to provide the initial conditions for numerical weather prediction (NWP). Desired fields include those of temperature, moisture, winds, clouds, and surface properties. For high horizontal resolution and global coverage, satellite data are an unrivaled source of information. The basic form of this information is satellite sensor‐incident, wavelength‐dependent radiance (or equivalently, brightness temperature). The process of retrieving meteorological information from the observed radiances involves solving an inverse problem. Often the inverse problem is ill posed or poorly conditioned. A variety of methods have been used to make the inverse problem more tractable: in essence, some a priori information is required. The inverse problem is the opposite of the calculation of radiances, given the relevant meteorological profiles, surface conditions, and sensor geometry. This so‐called forward problem is an integral part of physical retrieval methods which are now generally favored over conventional statistical retrieval methods. Physical retrieval methods, in practice, actually rely to a certain degree on a priori statistics. Temperature retrievals, especially in the southern hemisphere, are of proven importance in NWP involving global scale models. Vertical temperature profiles are obtained from observed radiances in a set of emission bands with varying optical depths. In comparison to conventional radiosonde data, current temperature retrievals have coarse vertical resolution. There is also some evidence that retrieved synoptic features are somewhat smoothed horizontally in spite of the high horizontal resolution of the temperature retrievals. The usefulness of other geophysical parameters for NWP is not well established and is largely untested, although a great many retrieval methods have been proposed or developed for a variety of potentially interesting parameters. The reason for this is twofold: these data are expected to have the most impact on mesoscale modeling, but mesoscale analysis systems are still relatively primitive. Moisture variables—that is, specific humidity, clouds, and precipitation—are retrievable and are potentially very useful. It is theoretically possible to retrieve specific humidity profiles by using methods analogous to those used to retrieve temperatures. However, results to date with available sensors have not been wholly satisfactory. Other moisture parameters are somewhat easier to retrieve; for example, cloud cover may be obtained by a variety of techniques from imagery in visible and/or infrared spectral regions, and vertically integrated water vapor is well correlated with emissions at microwave frequencies, especially over the ocean. Unfortunately, these parameters are not readily assimilated by a global NWP model, and special methods are required. A variety of other meteorological parameters which may be used for NWP are retrievable. These include winds at the surface from microwave sensors and cloud drift winds aloft, as well as o...

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Section: Random Instrumental Errors Of 3 M S-• In Each Wind Compo-mentioning

confidence: 99%

Satellite remote sensing of meteorological parameters for global numerical weather prediction

Isaacs¹,

Hoffman²,

Kaplan³

1986

Reviews of Geophysics

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Impact of initial conditions on seasonal simulations with an atmospheric general circulation model

Anderson

Ploshay

2000

Quart J Royal Meteoro Soc

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Many previous studies have examined the use of very long integrations of atmospheric general circulation models (AGCMs) forced by observed sea surface temperatures (SSTs) as proxies for seasonal atmospheric predictions. These long simulations explore a boundary-value problem in which significant deviations from the model's long-term climatology must be a result of the SST forcing. Seasonal lead simulations starting with observed initial conditions (ICs) for the atmosphere and land surface while retaining observed SST forcing are an intermediate step between the pure boundary-value problem and the pure initial-value forecast problem in which SSTs are also predicted. As part of the Dynamical Seasonal Prediction (DSP) experiment, an ensemble of AGCM integrations with observed atmospheric ICs and model climatology land surface ICs was integrated from mid-December through March for 16 years. These DSP simulation ensembles are compared to ensembles of long boundary-value simulations from the same AGCM in a perfect-model setting (no comparisons of simulations to observations are attempted). Significant differences must be due to the impact of the DSP ICs. Surprisingly large and long-lived differences are found in both the mean and the variance of the ensembles. Many appear to occur because the ICs of the DSP runs are inconsistent with the AGCM climatology; an extended period of model 'spinup' is the result. Some differences are related to local impacts of the land surface ICs while others, like shifts in the distribution of tropical precipitation and a cooling of the northern hemisphere, are less obviously related to the ICs. The results suggest that care will be needed when inserting observed ICs into seasonal predictions in order to avoid the long-term effects of model spin-up.

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The Inverse Problem for Hamilton--Jacobi Equations and Semiconcave Envelopes

Esteve¹,

Zuazua²

2020

SIAM J. Math. Anal.

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Initialization with the data assimilation method

Cited by 5 publications

References 43 publications

Satellite remote sensing of meteorological parameters for global numerical weather prediction

Satellite remote sensing of meteorological parameters for global numerical weather prediction

Impact of initial conditions on seasonal simulations with an atmospheric general circulation model

The Inverse Problem for Hamilton--Jacobi Equations and Semiconcave Envelopes

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