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North American Multi-Model Ensemble Teleconference

More on “failed” precipitation forecasts

June 14, 2018  Dr. Huug van den Dool of NWS Climate Prediction Center continued the discussion of the "unsuccessful" forecast of 2015/16 winter Southern California precipitation, following on the NMME teleconference led by Dr. Arun Kumar in April. Calibrated probabilistic forecasts (van den Dool et al. 2017), show improved reliability and resolution, but not predictability, which could be attributable to model deficiencies and shortcoming of the system such as signals captured by the models and the relatively short hindcast period. More in-depth thinking was explored, including (1) that the Southern California rainfall climate might be odd (intermittent, seasonal and non-Gaussian) and marginal, and the NMME models may not accurately represent the current Southern California climate; (2) interdecadal variations in ENSO/mid-latitude correlation could be poorly captured by the models; (3) eddy-mean flow interactions could impact the jet extension that reached the west coast of U.S. (e.g. 1982/83 vs. 2015/16); and (4) the noise could be changing, and may act "in our favor" on some occasions. An intense program of large-ensemble and shorter range forecasts was recommended for more insightful studies on the minority of model members that captured more accurate patterns for 2015/16. Lastly, Dr. van den Dool raised the question of how to make our probability forecasts a more effective and correct message in order to better serve our users. CPC forecasters are not predicting probabilities, but rather use probabilities to express uncertainty in the forecast (in lieu of error bars). Recalling the AMS statement from the 1980s on ethical standards for weather forecasting, “a forecast has to be unambiguous, reproducible and verifiable”, Dr. van den Dool made the point that a forecast should be correct or not correct, nothing in between.


van den Dool, H., E. Becker, L.-C. Chen, and Q. Zhang, 2017: The probability anomaly correlation and calibration of probabilistic forecasts. Wea. Forecasting, 32, 199-206.

Environmental Modeling Center Seminar

The emerging role of the land surface in weather and climate prediction

May 29, 2018  Professor Paul Dirmeyer of George Mason University gave a seminar on the emerging role of the land surface in weather and climate prediction and keys to assessing and improving model performance related to land-atmosphere interactions at the NOAA Center for Weather and Climate Prediction, College Park, MD. It was demonstrated that the land surface, a slowly varying manifold relative to the atmosphere, provides predictability and prediction skill across a range of time scales with the peak influence in the “subseasonal” time range between 1-3 weeks (Fig. 1). Looking for insight into the land-atmosphere feedback ingredients, i.e. sensitivity, variability and memory, Prof. Dirmeyer revealed a clear correlation between the efficiency of converting good land initial conditions into forecast improvement and the strength of the land-atmosphere coupling. There are significant impacts of land, or errors in its representation, beginning from the first morning of simulation via both terrestrial and atmospheric legs of the feedback pathway.

The process chains that link soil moisture, vegetation, snow, and other land states through the energy and water cycles manifest through their effects on the growing daytime boundary layer, cloud formation and convection. The seminar concluded that the daily, monthly and seasonal mean skill arising from coupled land-atmosphere feedbacks can only be improved by improving the diurnal cycle. System-level planning should be pursued for model development, calibration and validation (Dirmeyer et al. 2018).


Dirmeyer, P., and Co-authors, 2018: Verification of land-atmosphere coupling in forecast models, reanalyses, and land surface models using flux site observations. J. Hydrometeor., 19, 375-392. doi: 10.1175/JHM-D-17-0152.s1

Fig. 1 Illustration of predictability distribution of atmosphere, land and ocean in time scales from weather to climate, showing the prominent role played by land states (namely soil moisture but also snow) at subseasonal time scales.

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