GLORIA

GEOMAR Library Ocean Research Information Access

X

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • American Meteorological Society  (6)
  • Geography  (6)
Material
Publisher
  • American Meteorological Society  (6)
Person/Organisation
Language
Years
Subjects(RVK)
  • Geography  (6)
RVK
  • 1
    Online Resource
    Online Resource
    Probabilistic Precipitation Forecast Skill as a Function of Ensemble Size and Spatial Scale in a Convection-Allowing Ensemble
    American Meteorological Society ; 2011
    In:  Monthly Weather Review Vol. 139, No. 5 ( 2011-05), p. 1410-1418
    Details
    In: Monthly Weather Review, American Meteorological Society, Vol. 139, No. 5 ( 2011-05), p. 1410-1418
    Abstract: Probabilistic quantitative precipitation forecasts (PQPFs) from the storm-scale ensemble forecast system run by the Center for Analysis and Prediction of Storms during the spring of 2009 are evaluated using area under the relative operating characteristic curve (ROC area). ROC area, which measures discriminating ability, is examined for ensemble size n from 1 to 17 members and for spatial scales ranging from 4 to 200 km. Expectedly, incremental gains in skill decrease with increasing n. Significance tests comparing ROC areas for each n to those of the full 17-member ensemble revealed that more members are required to reach statistically indistinguishable PQPF skill relative to the full ensemble as forecast lead time increases and spatial scale decreases. These results appear to reflect the broadening of the forecast probability distribution function (PDF) of future atmospheric states associated with decreasing spatial scale and increasing forecast lead time. They also illustrate that efficient allocation of computing resources for convection-allowing ensembles requires careful consideration of spatial scale and forecast length desired.
    Type of Medium: Online Resource
    ISSN: 0027-0644 , 1520-0493
    URL: Article
    DOI: 10.1175/2010MWR3624.1
    RVK:
    RA 1000
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2011
    detail.hit.zdb_id: 2033056-X
    detail.hit.zdb_id: 202616-8
    SSG: 14
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 2
    Online Resource
    Online Resource
    The Impact of Assimilating Satellite-Derived Layered Precipitable Water, Cloud Water Path, and Radar Data on Short-Range Thunderstorm Forecasts
    American Meteorological Society ; 2021
    In:  Monthly Weather Review Vol. 149, No. 5 ( 2021-05), p. 1359-1380
    Details
    In: Monthly Weather Review, American Meteorological Society, Vol. 149, No. 5 ( 2021-05), p. 1359-1380
    Abstract: With the launch of GOES-16 in November 2016, effective utilization of its data in convective-scale numerical weather prediction (NWP) has the potential to improve high-impact weather (HIWeather) forecasts. In this study, the impact of satellite-derived layered precipitable water (LPW) and cloud water path (CWP) in addition to NEXRAD observations on short-term convective-scale NWP forecasts are examined using three severe weather cases that occurred in May 2017. In each case, satellite-derived CWP and LPW products and radar observations are assimilated into the Advanced Research Weather Research and Forecasting (WRF-ARW) Model using the NSSL hybrid Warn-on-Forecast (WoF) analysis and forecast system. The system includes two components: the GSI-EnKF system and a deterministic 3DEnVAR system. This study examines deterministic 0–6-h forecasts launched from the hybrid 3DEnVAR analyses for the three severe weather events. Three types of experiments are conducted and compared: (i) the control experiment (CTRL) without assimilating any data, (ii) the radar experiment (RAD) with the assimilation of radar and surface observations, and (iii) the satellite experiment (RADSAT) with the assimilation of all observations including surface-, radar-, and satellite-derived CWP and LPW. The results show that assimilating additional GOES products improves short-range forecasts by providing more accurate initial conditions, especially for moisture and temperature variables.
    Type of Medium: Online Resource
    ISSN: 0027-0644 , 1520-0493
    URL: Article
    DOI: 10.1175/MWR-D-20-0040.1
    RVK:
    RA 1000
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2021
    detail.hit.zdb_id: 2033056-X
    detail.hit.zdb_id: 202616-8
    SSG: 14
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 3
    Online Resource
    Online Resource
    Cloud-dependent piecewise assimilation based on a hydrometeor-included background error covariance and its impact on regional Numerical Weather Prediction
    American Meteorological Society ; 2021
    In:  Monthly Weather Review ( 2021-07-15)
    Details
    In: Monthly Weather Review, American Meteorological Society, ( 2021-07-15)
    Abstract: The background error covariance ( B ) behaves differently and needs to be carefully defined in cloudy areas due to larger uncertainties caused by models’ inability to correctly represent complex physical processes. This study proposes a new cloud-dependent B strategy by adaptively adjusting the hydrometeor-included B in the cloudy areas according to the cloud index (CI) derived from the satellite-based cloud products. The adjustment coefficient is determined by comparing the error statistics of B for the clear and cloudy areas based on the two-dimensional geographical masks. The comparison highlights the larger forecast errors and manifests the necessity of using appropriate B in cloudy areas. The cloud-dependent B is then evaluated by a series of single observation tests and three-week cycling assimilation and forecasting experiments. The single observation experiments confirm that the cloud-dependent B allows cloud dependency for the multivariate analysis increments and alleviates the discontinuities at the cloud mask borders by treating the CI as an exponent. The impact study on regional numerical weather prediction (NWP) demonstrates that the application of the cloud-dependent B reduces analyses and forecasts bias and increases precipitation forecast skills. Diagnostics of a heavy rainfall case indicate that the application of the cloud-dependent B enhances the moisture, wind, and hydrometeors analyses and forecasts, resulting in more accurate forecasts of accumulated precipitation. The cloud-dependent piecewise analysis scheme proposed herein is extensible, and a more precise definition of CI can improve the analysis, which deserves future investigation.
    Type of Medium: Online Resource
    ISSN: 0027-0644 , 1520-0493
    URL: Article
    DOI: 10.1175/MWR-D-20-0419.1
    RVK:
    RA 1000
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2021
    detail.hit.zdb_id: 2033056-X
    detail.hit.zdb_id: 202616-8
    SSG: 14
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 4
    Online Resource
    Online Resource
    Microwave Sounder Cloud Detection Using a Collocated High-Resolution Imager and Its Impact on Radiance Assimilation in Tropical Cyclone Forecasts
    American Meteorological Society ; 2016
    In:  Monthly Weather Review Vol. 144, No. 10 ( 2016-10), p. 3937-3959
    Details
    In: Monthly Weather Review, American Meteorological Society, Vol. 144, No. 10 ( 2016-10), p. 3937-3959
    Abstract: Accurate cloud detection is one of the most important factors in satellite data assimilation due to the uncertainties associated with cloud properties and their impacts on satellite-simulated radiances. To enhance the accuracy of cloud detection and improve radiance assimilation for tropical cyclone (TC) forecasts, measurements from the Advanced Microwave Sounding Unit-A (AMSU-A) on board the Aqua satellite and the Advanced Technology Microwave Sounder (ATMS) are collocated with high spatial resolution cloud products from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Aqua and the Visible Infrared Imager Radiometer Suite (VIIRS) on board the Suomi-National Polar-Orbiting Partnership ( Suomi-NPP ) satellite. The cloud-screened microwave radiance measurements are assimilated for Hurricane Sandy (2012) and Typhoon Haiyan (2013) forecasts using the Weather Research and Forecasting (WRF) Model and the three-dimensional variational (3DVAR)-based Gridpoint Statistical Interpolation (GSI) data assimilation system. Experiments are carried out to determine the optimal thresholds of cloud fraction (CF) for minimizing track and intensity forecast errors. The results indicate that the use of high spatial resolution cloud products can improve the accuracy of TC forecasts by better eliminating cloud-contaminated microwave sounder field-of-views (FOVs). In conclusion, the combination of advanced microwave sounders and collocated high spatial resolution imagers is able to improve the radiance assimilation and TC forecasts. The methodology used in this study can be applied to process data from other pairs of microwave sounders and imagers on board the same platform.
    Type of Medium: Online Resource
    ISSN: 0027-0644 , 1520-0493
    URL: Article
    DOI: 10.1175/MWR-D-15-0300.1
    RVK:
    RA 1000
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2016
    detail.hit.zdb_id: 2033056-X
    detail.hit.zdb_id: 202616-8
    SSG: 14
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 5
    Online Resource
    Online Resource
    A Modeling Case Study of Heavy Rainstorms along the Mei-Yu Front
    American Meteorological Society ; 1998
    In:  Monthly Weather Review Vol. 126, No. 9 ( 1998-09), p. 2330-2351
    Details
    In: Monthly Weather Review, American Meteorological Society, Vol. 126, No. 9 ( 1998-09), p. 2330-2351
    Type of Medium: Online Resource
    ISSN: 0027-0644 , 1520-0493
    URL: Article
    DOI: 10.1175/1520-0493(1998)126〈2330:AMCSOH〉2.0.CO;2
    RVK:
    RA 1000
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 1998
    detail.hit.zdb_id: 2033056-X
    detail.hit.zdb_id: 202616-8
    SSG: 14
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 6
    Online Resource
    Online Resource
    Sensitivity of Ensemble Forecast Verification to Model Bias
    American Meteorological Society ; 2018
    In:  Monthly Weather Review Vol. 146, No. 3 ( 2018-03), p. 781-796
    Details
    In: Monthly Weather Review, American Meteorological Society, Vol. 146, No. 3 ( 2018-03), p. 781-796
    Abstract: This study demonstrates how model bias can adversely affect the quality assessment of an ensemble prediction system (EPS) by verification metrics. A regional EPS [Global and Regional Assimilation and Prediction Enhanced System-Regional Ensemble Prediction System (GRAPES-REPS)] was verified over a period of one month over China. Three variables (500-hPa and 2-m temperatures, and 250-hPa wind) are selected to represent “strong” and “weak” bias situations. Ensemble spread and probabilistic forecasts are compared before and after a bias correction. The results show that the conclusions drawn from ensemble verification about the EPS are dramatically different with or without model bias. This is true for both ensemble spread and probabilistic forecasts. The GRAPES-REPS is severely underdispersive before the bias correction but becomes calibrated afterward, although the improvement in the spread’s spatial structure is much less; the spread–skill relation is also improved. The probabilities become much sharper and almost perfectly reliable after the bias is removed. Therefore, it is necessary to remove forecast biases before an EPS can be accurately evaluated since an EPS deals only with random error but not systematic error. Only when an EPS has no or little forecast bias, can ensemble verification metrics reliably reveal the true quality of an EPS without removing forecast bias first. An implication is that EPS developers should not be expected to introduce methods to dramatically increase ensemble spread (either by perturbation method or statistical calibration) to achieve reliability. Instead, the preferred solution is to reduce model bias through prediction system developments and to focus on the quality of spread (not the quantity of spread). Forecast products should also be produced from the debiased but not the raw ensemble.
    Type of Medium: Online Resource
    ISSN: 0027-0644 , 1520-0493
    URL: Article
    DOI: 10.1175/MWR-D-17-0223.1
    RVK:
    RA 1000
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2018
    detail.hit.zdb_id: 2033056-X
    detail.hit.zdb_id: 202616-8
    SSG: 14
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...