Atmospheric Predictability

Yoden, Shigeo

doi:10.2151/jmsj.85b.77

Cited by 34 publications

(27 citation statements)

References 150 publications

(140 reference statements)

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“…Bias correction by using mean error may help identify initial-value predictability. Moreover, although the method is not mathematically trivial (Yoden 2007), a multi-model ensemble mean without an erroneous mean tendency has better skill in seasonal forecasting than a single-model ensemble (Krishnamurti et al 1999;Palmer et al 2000). It is valuable to attempt the bias correction with mean tendency error in a multi-model ensemble; however, this is beyond the scope of this work.…”

Section: Summary and Discussionmentioning

confidence: 99%

Methods to Evaluate Prediction Skill in the Madden-Julian Oscillation Phase Space

Ichikawa

Inatsu

2016

Journal of the Meteorological Society of Japan

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Indices of prediction skill over the Madden-Julian oscillation (MJO) phase space are examined with reanalysis and forecast data provided by the Japan Meteorological Agency (JMA). In addition to the bivariate root-meansquare error (RMSE) and the bivariate anomaly correlation coefficient (ACC), the mean-error vector is assessed. Conventionally, the RMSE and ACC have been used, although this approach misses information on the model bias for MJO events. Moreover, the ACC is not suitable for models in which the MJO signal tends to damp in some phases, because the ACC strongly depends on the MJO amplitude. The mean-error vector compensates for this drawback by associating a model's erroneous mean tendency with the RMSE. For example, the JMA forecast produces a leftward mean error vector field uniformly distributed over the MJO phase space with its amplitude related to the RMSE. The RMSE should be then used with the mean error vector for evaluating the MJO prediction skill.

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Section: Summary and Discussionmentioning

confidence: 99%

Methods to Evaluate Prediction Skill in the Madden-Julian Oscillation Phase Space

Ichikawa

Inatsu

2016

Journal of the Meteorological Society of Japan

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“…Reasons of this difficulty are yielded from (1) errors in initial conditions as well as (2) inherent limit to predictability; (3) poor model dynamics and physics (Yoden 2007). Especially, water vapor in the initial condition affects the predicted rainfall amount, so it is important to improve water vapor distribution by data assimilation.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Rainfall Forecast by Assimilations of Ground-Based GPS Data and Radio Occultation Data

Seko¹,

Kunii²,

Shoji³

et al. 2010

SOLA

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Impacts of three kinds of GPS-derived water vapor data, i.e., precipitable water vapor (PWV), slant water vapor (SWV) and radio occultation (RO) data, were investigated using the Meso 4D-Var system of Japan Meteorological Agency (JMA) for a heavy rainfall case on 16 July 2004. When PWV or SWV data were assimilated individually, water vapor in the rainfall region was increased and on the northern sides was decreased, and then the shape of the rainfall region became similar to the observed one. However, the reproduced rainfall amount remained smaller than the observed one. Compared with PWV, SWV made the horizontal contrast of water vapor larger. When RO data were assimilated, the low-level water vapor was increased so that the rainfall amount was largely increased. However, the rainfall region became wider than the observed one. When SWV and RO data were assimilated simultaneously, low-level water vapor in the rainfall region and on its southern side was increased, and then both shapes of rainfall region and of rainfall amount became similar to the observed ones.

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“…As the solution in mesoscale models is integrated forward in time, the uncertainties associated with the errors in the initial conditions increase (Yoden, 2007). This can cause the model solution to drift away from the true solution.…”

Section: Simulation Timementioning

confidence: 99%

An intercomparison of mesoscale models at simple sites for wind energy applications

et al. 2017

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Abstract. Understanding uncertainties in wind resource assessment associated with the use of the output from numerical weather prediction (NWP) models is important for wind energy applications. A better understanding of the sources of error reduces risk and lowers costs. Here, an intercomparison of the output from 25 NWP models is presented for three sites in northern Europe characterized by simple terrain. The models are evaluated using a number of statistical properties relevant to wind energy and verified with observations. On average the models have small wind speed biases offshore and aloft ( < 4 %) and larger biases closer to the surface over land (> 7 %). A similar pattern is detected for the inter-model spread. Strongly stable and strongly unstable atmospheric stability conditions are associated with larger wind speed errors. Strong indications are found that using a grid spacing larger than 3 km decreases the accuracy of the models, but we found no evidence that using a grid spacing smaller than 3 km is necessary for these simple sites. Applying the models to a simple wind energy offshore wind farm highlights the importance of capturing the correct distributions of wind speed and direction.

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Atmospheric Predictability

Cited by 34 publications

References 150 publications

Methods to Evaluate Prediction Skill in the Madden-Julian Oscillation Phase Space

Methods to Evaluate Prediction Skill in the Madden-Julian Oscillation Phase Space

Improvement of Rainfall Forecast by Assimilations of Ground-Based GPS Data and Radio Occultation Data

An intercomparison of mesoscale models at simple sites for wind energy applications

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