Jan 30, 2014

OSNW3 Trend Comparison to the Jeremy Nelson LRC Based Long Range Forecast

I wanted to analyze the OSNW3 long range numbers for February in conjunction to Jeremy Nelson's forecast for southeastern WI. Jeremy is a meteorologist at WISN12 in Milwaukee who uses LRC techniques to long range forecast. His "February forecast and beyond" can be found at this link.

I will highlight some pieces of the forecast.

1. "The pattern looks to stay busy with a chance of snow around February 4-5 and 7-8 [...] some of the storm systems will have more moisture to work with" - February 1-7 Section (Black)

2. "Following each snow chance a quick hit of cold air will sweep across southeastern Wisconsin. More below zero lows are likely this month. Temperatures should moderate and possibly move above average late in week two of February [...] The average/above average temperatures should last 1-3 days" - February 8-15 Section (Orange)

3. "The middle of the month should see the return of one of the biggest precipitation producers for southeast Wisconsin [...] As this feature returns around February 14-17 another storm system is possible impacting our area" - February 16-28 Section (Blue)

4. "Closing out the month a moderation in temperatures is possible around February 20-22 before another surge of cold air around Feb 23-25. A couple chances for snow showers also possible the final week of February" - February 16-28 Section (Red)

The image below is a snap shot of the Milwaukee, WI trend from the OSNW3|WxClimate website for February. I highlighted the points mentioned above in the image. For Jeremy's entire forecast be sure to click the blog link above. Click here for more information on the image below, which will provide an interactive experience for the Brewer's home opener Jeremy mentions in his forecast as well.

If there are any questions, comments, or suggestions on the material presented please let me know. Thanks for reading!

Jan 25, 2014

Translating Tropical ISO to the Midlatitudes

Studies have attempted to identify recurrent modes that contribute to the alternation between active and inactive periods of the Indian summer monsoon and the South China Sea summer monsoon. Generally, two components of variability were found to be dominant on an intraseasonal time scale: the “short-term” component, with a period of less than 1 month, and the “long-term” component, with a period of 1–2 months but apparently less than a season. Both can be referred to as the intraseasonal oscillations (ISOs) since they are clearly separated from the high-frequency synoptic-scale (less than 10 days) features and the slowly varying seasonal cycle as well. - BIN GUAN AND JOHNNY C. L. CHAN

We considered our cycle correlation a step in the right direction. Next was to match the cycle correlation to the "short-term" and "long-term" ISO components. The paper referenced above identified the years 1981, 1984, and 1990 as case studies of when either the 10-20 or 30-60 day ISO was strong. The paper states 1981 is strong in the "short-term" 10-20 day ISO, 1984 is strong in the "long-term" 30-60 day ISO, and 1990 is strong in the seasonal cycle.

For each of the case studies we think our cycle correlation matches their findings. 1981 does favor the "short-term" component. We see positive and negative correlation in the 15 day time period. 1984 does favor the "long-term" component. We see one strong lengthy correlation at days 41-47. The 1990 seasonal cycle is inconclusive, however. We could debate either way, short and long, but would need a wider time scale to make a seasonal cycle determination. What we label as the "harmonic", they label as the "short-term" 10-20 day ISO. What we label as the "cycle", they label as the "long-term" 30-60 day ISO. Because the findings match, we can assume that our cycle correlation method is valid and can piggy back the paper as something significant and scientifically valid.

Tropical intraseasonal oscillation (ISO), especially its dominant component at the equator—Madden–Julian oscillation (MJO), has been extensively studied over the past decades. ISO (or MJO) consists of large-scale coupled patterns in atmospheric circulation and deep convection [...] the influence of MJO can extend to the subtropics and midlatitudes. Associated with enhanced (reduced) tropical convection, the upper-level divergence (convergence) is often accompanied by a subtropical upper-level convergence (divergence) counterpart. - Li LIN-LIN PAN AND TIM LI

Extending the tropical ISO into the midlatitude we are able to find the "short-term" and "long-term" ISO (see image below). Applying our cycle correlation to the h5 pattern since October 1, 2013 we see a strong correlation in the "short-term" component around 15 days. Using principles of standing wave harmonics we can then solve the "long-term" component. Evolution of the sine wave shows peak to peak harmonics of around 15 days. More to come.

If there are any questions, comments, or suggestions on the material presented please let me know. Thanks for reading!

Jan 19, 2014

Seeking an Auto Discovery Cycle Duration

A while back I needed a quick way to compare soundings. I coded a few simple VBA instructions to do this. I was happy, I could study daily soundings from many stations and come up with a perceived cycle length. Using this tool became stale however and I felt I was staring and comparing like I had been doing with maps in the past. With some statistical motivation I am now seeking correlation in the sounding data.

Admittedly I had no idea what was going to result from correlating two cycles of sounding data. I gathered the data and looped it through a 30-60 day period. To the surprise of myself and another, it produced a spectacular looking sine wave. The data represent 20 sounding stations throughout the states of ND, SD, NE, KS, MO, IA, MN, WI, MI, IL, OH, PA, NY and MA. Station locations are color coded. Purple represents the region that is farthest west. Red represents the region that is the farthest east. The top chart displays the regional average correlation. The bottom chart displays the individual station correlation. This particular sample time frame begins 10/1/2006.

Taking the absolute value of the correlation coefficient measures the strength of the relationship. A correlation coefficient of 0.50 indicates a stronger degree of linear relationship than one of 0.40. Likewise a correlation coefficient of -0.50 shows a greater degree of relationship than one of -0.40. Thus a correlation coefficient of zero (0.0) indicates the absence of a linear relationship and correlation coefficients of +1.0 and -1.0 indicate a perfect linear relationship.

With this application the positive, zero, and negative values each hold significant meaning in regard to standing wave harmonics. The results prove to me that cycle duration with auto discovery is obtainable. More to come.

If there are any questions, comments, or suggestions on the material presented please let me know. Thanks for reading!