Are greenhouse gas signals of Northern Hemisphere winter extra-tropical cyclone activity dependent on the identification and tracking algorithm?

For Northern Hemisphere extra-tropical cyclone activity, the dependency of a potential anthropogenic climate change signal on the identification method applied is analysed. This study investigates the impact of the used algorithm on the changing signal, not the robustness of the climate change signal itself. Using one single transient AOGCM simulation as standard input for eleven state-of-the-art identification methods, the patterns of model simulated present day climatologies are found to be close to those computed from re-analysis, independent of the method applied. Although differences in the total number of cyclones identified exist, the climate change signals (IPCC SRES A1B) in the model run considered are largely similar between methods for all cyclones. Taking into account all tracks, decreasing numbers are found in the Mediterranean, the Arctic in the Barents and Greenland Seas, the mid-latitude Pacific and North America. Changing patterns are even more similar, if only the most severe systems are considered: the methods reveal a coherent statistically significant increase in frequency over the eastern North Atlantic and North Pacific. We found that the differences between the methods considered are largely due to the different role of weaker systems in the specific methods

IMILAST: a community effort to intercompare extratropical cyclone detection and tracking algorithms

The variability of results from different automated methods of detection and tracking of extratropical cyclones is assessed in order to identify uncertainties related to the choice of method. Fifteen international teams applied their own algorithms to the same dataset—the period 1989–2009 of interim European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERAInterim) data. This experiment is part of the community project Intercomparison of Mid Latitude Storm Diagnostics (IMILAST; see www.proclim.ch/imilast/index.html). The spread of results for cyclone frequency, intensity, life cycle, and track location is presented to illustrate the impact of using different methods. Globally, methods agree well for geographical distribution in large oceanic regions, interannual variability of cyclone numbers, geographical patterns of strong trends, and distribution shape for many life cycle characteristics. In contrast, the largest disparities exist for the total numbers of cyclones, the detection of weak cyclones, and distribution in some densely populated regions. Consistency between methods is better for strong cyclones than for shallow ones. Two case studies of relatively large, intense cyclones reveal that the identification of the most intense part of the life cycle of these events is robust between methods, but considerable differences exist during the development and the dissolution phases

Fitting Precipitation Particle Size-Velocity Data to Mixed Joint Probability Density Function with an Expectation Maximization Algorithm

This paper proposes an estimation method of joint size and terminal velocity distribution on the basis of sampling data of precipitation particles containing multiple types. Assuming that the velocity follows the normal distribution and the size follows the gamma distribution, the method searches a locally maximum logarithmic likelihood within a realistic parameter range using the expectation-maximization algorithm. Several test populations were prepared with a realistic number of elements, and then the method was evaluated by retrieving the populations from their sample. The results showed that the original parameters were successfully estimated in most cases of the test population containing some of liquids, graupels, and rimed and unrimed aggregates. The original number of elements was also estimated with an adjustment of the number of elements in a manner such that each of their minority fractions exceeded a threshold. Applied to the two-dimensional disdrometer observation data, the method was helpful to discard frequently observed erroneous data with unrealistically large fall velocity

Wintertime extratropical cyclone frequency around Japan

Data analysis and regional atmospheric model (RAM) experiments revealed key factors in the control of wintertime cyclone passage routes from Northeast Asia to the western North Pacific. The cyclone routes were independent of the global flow pattern in the interannual variability, while cyclone growth closely agreed with linear baroclinic theory. The RAM experiments with a different lateral boundary condition composed of a combination of monthly mean and transient components also showed that the upstream eddies are important for the track route, but the background states are not. Additionally, the RAM experiments showed that the mean flow controlled the growth rate of cyclones

Dynamics and Practical Predictability of Extratropical Wintertime Low-Frequency Variability in a Low-Dimensional System

Dynamics and practical predictability of extratropical low-frequency variability (LFV) in Northern Hemisphere winter are examined in the framework of a two-dimensional (2D) stochastic differential equation (SDE) on the phase space spanned by two leading empirical orthogonal function modes of low-pass-filtered 500-hPa geopotential height variations. The drift vector and diffusion tensor of the 2D SDE with multiplicative noise are theoretically connected with deterministic and stochastic error growth, respectively; both are statistically estimated from a reanalysis dataset. Projected onto the 2D phase space is the practical predictability of the LFV estimated by the 10-day forecast spread based on the 1-month ensemble prediction operationally conducted by the Japan Meteorological Agency (JMA). It is shown that the forecast spread of the LFV prediction by the JMA model for a relatively shorter prediction period when the model bias does not hamper the forecast is primarily explained by the stochastic error growth associated with the diffusion tensor and the deterministic error growth due to the Jacobian of the drift vector plays a secondary role. A non-Gaussian PDF of the LFV is also related to the norm of the diffusion tensor. Hence, the stochastic processes mostly control the dynamics and predictability of the LFV in the 2D phase space

Predictability of Heavy Snowfall Days in Western Hokkaido from JMA Operational 1-Month Ensemble Predictions Using Self-Organizing Maps

We investigated the sub-seasonal predictability of heavy snowfall events in Iwamizawa, Hokkaido, using the Japan Meteorological Agency's 1-month ensemble predictions. First, the self-organizing map (SOM) technique was applied to the Japanese 55-year Reanalysis sea-level pressure anomalies to identify weather patterns resulting in heavy snowfall. It revealed that heavy snowfall developed in SOM nodes (weather patterns) with low-pressure centers to the east/northeast of Hokkaido and Siberian high to the west, resulting in westerly to northwesterly monsoon winds traversing the Sea of Japan towards western Hokkaido. Next, ensemble forecasts were projected onto the SOM map to determine the predictability of weather patterns up to a month in advance. For winter 2019, there was relatively low probability of projecting a high number of ensembles in SOM nodes to those observed in the reanalysis. In contrast, much higher probability was seen in 2020 to similar to 10 forecast days. When considering multiple SOM nodes that contribute to heavy snowfall in the forecast, both winters saw more ensemble members predicting heavy snowfall to similar to 10 forecast days. We also saw a higher probability of heavy snowfall beyond 10-days in 2020. These results highlight the potential benefit of incorporating multiple weather patterns to forecast heavy snowfall

An Alternative Estimate of Potential Predictability on the Madden–Julian Oscillation Phase Space Using S2S Models

This study proposes an alternative method to estimate the potential predictability without assuming the perfect model. A theoretical consideration relates a maximum possible value of the initial-value error to the covariance between analysis and bias-corrected ensemble-mean forecast. To test the method, the prediction limit of the Madden–Julian Oscillation (MJO) was evaluated, based on three pairs of reanalysis and forecast datasets provided by the European Centre for Medium-Range Weather Forecasting, the Japan Meteorological Agency and the National Centers for Environmental Prediction, participating in the subseasonal-to-seasonal prediction project. The results showed that the predictability was higher when MJO amplitude exceeded unity, consistent with the conventional method in which the error is evaluated as the ensemble-forecast spread. Moreover, the multimodel analysis was also conducted because the proposed method is readily applicable to the multimodel average of ensemble-mean forecasts. The phase dependency of the MJO’s potential predictability is also discussed