Satellite cloud and precipitation assimilation at operational NWP centres

Bauer, Péter; Auligné, Thomas; Bell, William; Geer, Alan; Guidard, Vincent; Heilliette, Sylvain; Kazumori, Masahiro; Kim, Minjeong; Liu, Emily H.-C.; McNally, A. P.; Macpherson, Bruce; Okamoto, Kozo; Renshaw, Richard; Riishojgaard, L.

doi:10.1002/qj.905

Cited by 120 publications

(106 citation statements)

References 83 publications

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“…Increasingly, NWP centres are making use of these observations in cloudy and precipitating situations as well as in clear skies (e.g. Bauer et al, 2011). This helps to infer water vapour information in cloudy and precipitating areas and it also gives the possibility to assimilate the cloud and precipitation itself.…”

Section: Introductionmentioning

confidence: 99%

Improved scattering radiative transfer for frozen hydrometeors at microwave frequencies

2014

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Abstract. To simulate passive microwave radiances in allsky conditions requires better knowledge of the scattering properties of frozen hydrometeors. Typically, snow particles are represented as spheres and their scattering properties are calculated using Mie theory, but this is unrealistic and, particularly in deep-convective areas, it produces too much scattering in mid-frequencies (e.g. 30-50 GHz) and too little scattering at high frequencies (e.g. 150-183 GHz). These problems make it hard to assimilate microwave observations in numerical weather prediction (NWP) models, particularly in situations where scattering effects are most important, such as over land surfaces or in moisture sounding channels. Using the discrete dipole approximation to compute scattering properties, more accurate results can be generated by modelling frozen particles as ice rosettes or simplified snowflakes, though hexagonal plates and columns often give worse results than Mie spheres. To objectively decide on the best particle shape (and size distribution) this study uses global forecast departures from an NWP system (e.g. observation minus forecast differences) to indicate the quality of agreement between model and observations. It is easy to improve results in one situation but worsen them in others, so a rigorous method is needed: four different statistics are checked; these statistics are required to stay the same or improve in all channels between 10 GHz and 183 GHz and in all weather situations globally. The optimal choice of snow particle shape and size distribution is better across all frequencies and all weather conditions, giving confidence in its physical realism. Compared to the Mie sphere, most of the systematic error is removed and departure statistics are improved by 10 to 60 %. However, this improvement is achieved with a simple "one-size-fits-all" shape for snow; there is little additional benefit in choosing the particle shape according to the precipitation type. These developments have improved the accuracy of scattering radiative transfer sufficiently that microwave all-sky assimilation is being extended to land surfaces, to higher frequencies and to sounding channels.

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Section: Introductionmentioning

confidence: 99%

Improved scattering radiative transfer for frozen hydrometeors at microwave frequencies

2014

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“…Rainfall forecast from the NWP models has improved over the last decades with the continuous progress in both numerical model and data assimilation techniques [Benedetti et al, 2005;Bauer et al, 2011;Lopez, 2011]. However, still there is a need to explore the new opportunities for model development through the use of data from active and passive sensors jointly [Benedetti et al, 2005;Pu et al, 2002;Bauer et al, 2011]. Rainfall assimilation is considered as one of the important approaches to improve the weather forecasts, because rainfall observation includes the atmospheric information in terms of humidity, temperature, and winds and also contributes to the model atmospheric energy budget [Marecal and Mahfouf, 2003].…”

Section: Introductionmentioning

confidence: 99%

“…Several numerical modeling studies have shown that rainfall data improved the weather forecast [e.g., Bauer et al, 2011;Krishnamurti et al, 1991Krishnamurti et al, , 1993Lopez, 2011;Puri and Miller, 1990;Treadon, 1996;Tsuyuki, 1997;Zou and Kuo, 1996;Zupanski and Mesinger, 1995]. A number of sensitivity tests have been performed for rainfall assimilation at various forecast/research centers like in European Centre for Medium-Range Weather Forecasts [Heckley et al, 1990;Puri and Miller, 1990], Florida State University [Krishnamurti et al, 1984[Krishnamurti et al, , 1993Tsuyuki, 1997], and National Centers for Environmental Prediction (NCEP) [Treadon, 1996[Treadon, , 1997.…”

Section: Introductionmentioning

confidence: 99%

“…Assimilation of TMI rainfall improves the global model analysis [Hou et al, 2004] that subsequently impacts the mesoscale model forecasts, as the global model provides the initial and boundary conditions for the mesoscale model [Pu et al, 2002]. Recently, rain rate assimilation became operational in 3-D-Var at the NCEP and in 4-D-Var at the Japan Meteorological Agency [Bauer et al, 2011]. TRMM 3B42 and Japan Aerospace Exploration Agency (JAXA) Global Satellite Mapping of Precipitation (GSMap)-derived merged rainfall data are used in this study.…”

Section: Introductionmentioning

confidence: 99%

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Impact of satellite rainfall assimilation on Weather Research and Forecasting model predictions over the Indian region

2014

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Rainfall is probably the most important parameter that is predicted by numerical weather prediction models, though the skill of rainfall prediction is the poorest compared to other parameters, e.g., temperature and humidity. In this study, the impact of rainfall assimilation on mesoscale model forecasts is evaluated during Indian summer monsoon 2011. The Weather Research and Forecasting (WRF) model and its four-dimensional variational data assimilation system are used to assimilate the Tropical Rainfall Measuring Mission 3B42 and Japan Aerospace Exploration Agency Global Satellite Mapping of Precipitation retrieved rainfall. A total of five experiments are performed daily with and without assimilation of rainfall data during the entire month of July 2011. Separate assimilation experiments are performed to assess the sensitivity of WRF model forecast with strict and less strict quality control. Assimilation of rainfall improves the forecast of temperature, specific humidity, and wind speed. Domain average improvement parameter of rainfall forecast is also improved over the Indian landmass when compared with NOAA Climate Prediction Center Morphing technique and Indian Meteorological Department gridded rainfall.

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“…For example, cloud water and precipitation from satellite sensors are now routinely ingested into numerical weather prediction (NWP) models (Bauer et al, 2011). Soil moisture observations are at the point of entering into operational NWP assimilation schemes (Rosnay et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Spatial patterns in timing of the diurnal temperature cycle

Holmes

Crow

Hain

2013

Hydrol. Earth Syst. Sci.

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Abstract. This paper investigates the structural difference in timing of the diurnal temperature cycle (DTC) over land resulting from choice of measuring device or model framework. It is shown that the timing can be reliably estimated from temporally sparse observations acquired from a constellation of low Earth-orbiting satellites given record lengths of at least three months. Based on a year of data, the spatial patterns of mean DTC timing are compared between temperature estimates from microwave Ka-band, geostationary thermal infrared (TIR), and numerical weather prediction model output from the Global Modeling and Assimilation Office (GMAO). It is found that the spatial patterns can be explained by vegetation effects, sensing depth differences and more speculatively the orientation of orographic relief features. In absolute terms, the GMAO model puts the peak of the DTC on average at 12:50 local solar time, 23 min before TIR with a peak temperature at 13:13 (both averaged over Africa and Europe). Since TIR is the shallowest observation of the land surface, this small difference represents a structural error that possibly affects the model's ability to assimilate observations that are closely tied to the DTC. The equivalent average timing for Ka-band is 13:44, which is influenced by the effect of increased sensing depth in desert areas. For non-desert areas, the Ka-band observations lag the TIR observations by only 15 min, which is in agreement with their respective theoretical sensing depth. The results of this comparison provide insights into the structural differences between temperature measurements and models, and can be used as a first step to account for these differences in a coherent way.

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Satellite cloud and precipitation assimilation at operational NWP centres

Cited by 120 publications

References 83 publications

Improved scattering radiative transfer for frozen hydrometeors at microwave frequencies

Improved scattering radiative transfer for frozen hydrometeors at microwave frequencies

Impact of satellite rainfall assimilation on Weather Research and Forecasting model predictions over the Indian region

Spatial patterns in timing of the diurnal temperature cycle

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