HURRICANE DORAIN OF 2019 AND EARLIER MAJOR HURRICANES

       On Monday, September 2, 2019 Hurricane Dorian obliterated much of the Bahamas. Maximum winds reached 185 mile per hour as the storm stalled near Freeport. The distance from us was 177 miles. It was uncertain then where the storm would go when steering currents began to guide it again. Most models had it coming close to our home with winds down to about 130 miles per hour. In 2017 we were without power for 43 hours due to Hurricane Irma. That's no fun in Florida's heat. So rather than fight traffic for 490 miles to Atlanta, we decided to go on a vacation. I bought three tickets on Southwest Airlines and, with my wife and son, flew off to Puerto Rico which Hurricane Dorian had just missed. We visited gorgeous beaches in San Juan and Mar Chiquita Cove, explored the Arecibo Radio Telescope (see Figure 4), and thoroughly enjoyed the rain forests which in places had vegetation that looked like it had come from some other exotic world.

       As for finding this storm in the Torah Code, the original axis term on Figure 1 was BAHAMAS. Normally it would display vertically, but I chose to show HURRICANE vertically with STALLED at the same skip. To do this the computer bases the display on 3,484 letters (the skip of HURRICANE) on each line in its memory rather than 6,965 (the skip of BAHAMAS). DORIAN shares a letter alef with BAHAMAS. A transliteration of ABACO touches BAHAMAS. Two numbers are highlighted - FIVE and THOUSAND. The storm was Category Five (winds of at least 157 miles per hour). Although the official death toll by October 4, 2019 was only at 56 over 600 people were still missing, so the death toll could have been as high as a thousand people. Seventy thousand people lost their homes. This terrible storm was the second strongest storm in Atlantic history, and TERRIBLE is in the open text.

STATISTICAL SIGNIFICANCE OF THE MATRIX. Normally the axis term is the first word sought and, unless a row split function is enabled, the letters of this term are shown vertically with no intermediate rows. On Figure 1 the 8-letter axis term is BAHAMAS at its 17th lowest skip in unwrapped Torah (a single computer pass through Torah). However in order to show the name of the storm (DORIAN) I had to employ a row skip of 2. Encoding is more obvious when a priori key words are at the absolute skip of the axis term, or at skips +/- 1. In this case we find TERRIBLE in the open text (skip +1) and THOUSAND at skip -1, but no term at the skip of BAHAMAS. However, HURRICANE is six letters (the minimum length that I normally allow for an axis term), and STATIONARY has the same skip as it. Therefore I also checked for the statistical significance of the matrix with HURRICANE used as the axis term. However, instead of dividing the combined significance of terms by 17 due to ELS rank 17 for BAHAMAS, we must now divide combined significance of terms by 76 because that is the ELS rank of HURRICANE. Despite the large divisor (76), treating HURRICANE as axis term leads to a higher statistical significance.

       With BAHAMAS as the axis term the most significant a priori term (HURRICANE) is found against odds of about 158 to 1. There are no other terms found against odds of at least 10 to 1, and  STATIONARY is of little value because this 4-letter Hebrew word (3 synonyms were checked for it) had over a 99.98% chance to be somewhere on the matrix. After factoring in ELS rank 17 of BAHAMAS, the entire matrix was found against odds of about 694 to 1. Yawn. HOWEVER, when we use HURRICANE as the axis term, the most significant term is BAHAMAS which is found against odds of about 326 to 1. Further, STATIONARY had only a 6.4% chance to be at a special case skip (in this case, the same skip as HURRICANE) somewhere on the matrix. The rest of the odds are unchanged, but after the required division by 76 the quotient shows that the matrix exists against odds of about 4,814 to 1. I could have shown the Hebrew year of the storm, but that takes the matrix size over 1,000 letters in area, and I really don't like doing that for one more term unless it is at a special case skip. The year does not appear that way so I'll leave the matrix as shown. 

CAN THE TORAH CODE HELP US TO PREDICT HURRICANES?

       Dr. Robert Haralick found the matrix  on Figure 2 after Hurricane Andrew. Note that for modern terms that are transliterations of English words (like hurricane) he accepts multiple spellings. I cannot address other languages, but it is quite apparent that the Torah's Encoder was (or will be) proficient in English, however that English is expressed in transliterations using Hebrew letters. Note: Dr. Haralick wrote the Foreword of my Ark Code book.