Krieger Weather Model

Abstract

Krieger Weather Model Figure to be published with the abbreviation: Fig. 1

Description

Title of the invention: Krieger Weather Model

[0001] The invention relates to a model derived from a discovery based on observations of crops and the living beings that surround them. This invention makes it possible to explain past meteorology and to anticipate future meteorological trends. This model also makes it possible to consider a new approach to global warming.

[0002] Living organisms have special relationships with the plant world. Their development cycles are aligned with plant growth, and the vast majority of them are sensitive to changes in weather conditions. Observing the behavior of living organisms evolving around crops offers a new and innovative avenue for understanding meteorology, anticipating weather forecasts, and considering a new approach to climate change.

[0003] Crops, that is, plants used by humans to meet their needs, are the best known and described plants. The same plants are sown or planted every year. They are the subject of numerous observations and experiments. The living organisms that come to obtain their source of energy and nutrients from them are also observed and generally understood. The biology of these organisms, however, is more or less well described. The main outlines of their life cycle can easily be found in the literature. Nevertheless, the climatic parameters (temperature, humidity, rainfall) influencing the output of these organisms and their development dynamics are, for the most part, still unknown. Crop organisms react to humidity and temperature conditions that fall within ranges that are difficult for humans to study precisely.They react differently from year to year, and humans are currently unable to explain all the parameters that govern the development of these predators. Hence the need for regular observations each year.

[0004] Each crop has its own complex of pests, fungi, or pathogens. These crop predators are subject to cycles. Every year, they are generally present and observable. Depending on the weather conditions of a given year, some predators will be more active, meaning they will be present in large numbers, be very active, and thus cause measurable losses in yield or even quality of crops.

[0005] The discovery consisted of uncovering this cycle of return of crop collectors in the Saint-Quentin area of ​​Aisne, France. This discovery is extrapolable to France.

[0006] Each year, in rapeseed cultivation, monitoring by trapping with weekly data collection is carried out to track insect emergence. Thus, over a 12-year period, an insect called the rapeseed stem weevil, belonging to the order Coleoptera, was monitored. In February 2008, this insect emerged unusually early. Its peak occurred around Wednesday, February 13, 2008, following high temperatures on February 11, 12, and 13, 2008. Subsequently, for the next 10 years, its peak occurred in the first ten days of March. Then in February 2019, an episode of exceptionally high temperatures occurred, reminiscent of February 2008. Thus, following high temperatures from February 21 to 28, 2019, a peak in the emergence of this insect took place around February 23 and 24, 2019. Spring 2019 is therefore similar to the spring of 2008.

[0007] [Tables 1] dated exit êstïméë weevil from Its stem of the catea (St Quentin} 2008 2009 2010 2011 2812 2013 2014 2015 2016 | 2817 |æ1«| 2019 February 13 March March wn March mare g March 6 / 7 March 8 March il 3 1283 11 / 12 j 16 | March 23 / 24 mm |mars| February

[0008] The winter and spring of 2020 are similar to the winter and spring of 2010. Indeed, following a dry and low-rain spring, in each of these two years, significant aphid populations on foliage and then on ears were observed in winter wheat.

[0009] The winter and spring of 2021 are similar to the winter and spring of 2012. In February 2012, a significant pressure from flea beetle larvae, a beetle that overwinters in rapeseed plants, exacerbated frost damage to the rapeseed. Even well-developed rapeseed plants were damaged by frost due to the presence of these larvae. In 2021, following frosts in February, similar frost damage to rapeseed was observed. Furthermore, on winter wheat, during the springs of 2012 and 2021, an exceptional presence of fungi of the genus Microdochium spp. was observed on the foliage.

[0010] Winter and spring 2022 are similar to winter and spring 2014. A significant and very early presence of a fungus called yellow rust on winter wheat was observed in 2022 and 2014.

[0011] Autumn 2022 is similar to autumn 2015. Indeed, in autumn 2022, a significant flight of autumn aphids transmitting a virus called Barley Yellow Dwarf Virus was observed on winter wheat. The last year with such a large flight was observed during the autumn of 2015.

[0012] The winter and spring of 2023 are similar to the winter and spring of 2016. A significant presence of a fungus called Septoria leaf blotch was indeed observed on winter wheat in 2023 and 2016. Furthermore, on winter barley, a significant presence of an uncommon fungus called Ramularia leaf blotch was indeed observed during the springs of 2023 and 2016.

[0013] The model is based on repeated annual observations of the behaviour of certain indicator samplers from 2002 to the present in correlation with meteorological data.

[0014] Meteorology often uses temperatures as a key to presentation. Temperatures are linked to enormous air masses, and the temperature differences between two neighboring locations are generally small. Thus, at any given point on Earth, temperatures fluctuate throughout the seasons, yet remain within the same range each year due to the climate associated with that region. The climate of northern France will not suddenly become the climate of southern France. This criterion was not considered sufficiently relevant in the model for interpreting meteorology.

[0015] On the other hand, rainfall in a given location is much more localized. Each year, mini-sectors within a larger area can be described according to the cumulative rainfall, with varying observable impacts on the behavior of crops, pests, fungi, and pathogens.

[0016] Based on annual observations over several years from indicator samplers of crops, the model is built on the discovery that the rainfall of a given year could be described by the rainfall of two half-years while keeping the month-by-month structure: a first rainfall period of 6 months of year n going from January to June followed by a second rainfall period of 6 months of year n+1 going from July to December.

[0017] [Tables2] Rainfall Year Rainfall of explanatory years Explained by 1st semester 2028 Explained by 1st semester 2028 and 2nd semester 2029 2028 Explained by 1st semester 2026 and 2nd semester 2027 2027 1st semester 2024 and 2nd semester 2025 2028 Explained by 1st semester 2022 and 2nd semester 2023 2025 Explained by 1st semester 2020 and 2nd semester 2021 2024 Explained by 1st semester 2018 and 2nd semester 2019 2023 1st semester 2016 and 2nd semester 2017 2022 Explained by 1st semester 2014 and O'"8 semester 2015 2021 Explained by first semester 2012 and second semester 2013 2023 r; semester 2010 and second semester 2011 2019 Explained by first semester 2008 and S''3 semester 2009 2018 Explained by first semester 2006 and S"8 semester 2007 201? Explained by first semester 2004 and second semester 2005 2016 Explained by S! semester 2002 and third semester 2003 2015 Explained by first semester 2000 and second semester 2001 2014 Explained by first semester 1938 and ^.Semester 1999-2013 Explained by the first semester of 1996 and the second semester of 1997-2012 Explained by the first semester of 1994 and the third semester of 1995-2011 Explained by the first semester of 1992 and the second semester of 1993-2010 Explained by the first semester of 1990 and the second semester of 1991-2003 Explained by the first semester of 1988 and the second semester of 1989-2003 Explained by the first semester of 1986 and the second semester of 1987-2007 Explained by the first semester of 1984 and the second semester of 1985-2008 First semester of 1982 and the second semester of 1983-2005 Explained by the first semester 19.80 and 21st semester 1981 2004 Explained by rr semester 1978 and 2nd semester 1979 2003 Explained® by L" semester 1976 and .283 semester -1977 .

[0018] Rainfall measurements will serve as a basis for the creation of the Krieger weather model.

[0019] Sunshine durations, minimum and maximum temperatures, the number of rainy days greater than 1mm, the number of rainy days less than or equal to 1mm are also recorded to consider the approach on global warming.

[0020] Meteorological data, including rainfall, the number of days with rainfall exceeding 1 mm, the number of days with rainfall less than or equal to 1 mm, sunshine duration, and minimum and maximum temperatures, are formatted in a spreadsheet, year by year and day by day, by city or village, or by weather station, available on the Internet or from any other information source. The databases start with the oldest recorded meteorological data and end with the most recent available meteorological data.

[0021] The examples shown in the tables are from the Météo France meteorological station of Saint Quentin (Roupy, department 02) in 2022.

[0022] Using the spreadsheet, month by month, for a given year, the data are compiled by parameter rainfall, number of rainy days greater than 1mm and number of rainy days less than or equal to 1mm, sunshine, maximum and minimum temperatures.

[0023] [Tables3] 2022 2022 2022 2022 2022 2022 2022 2022 2022 2022 2022 2022 Samf Quentm, Roupy K j.'VJ | FgV Wheatpiipiiotis «T ~ 2022 *°4 43 6 ia.4 s AVfis L SAi •> 27.7 i 31.9 sim; 62.5 11.5 F 11.8 | 86.6 | 34.6 | 48.5 in mm 2022 s 5 5 5 i 5 9 2 j 3 | 13 ! 7 10 § 6 Mb pints « a __aoaa___ b ... . , ' f Mean . „ msx. M22 08 | Average . | min. 2022 ; 82.1 ? | 10 / 1 3 13.7 3- i S 15.3 20.9 4 23.7 I li 1 * 2 i 6 | 2 | 2.9 3.1 13'0' 4.9 î 9.1 "2'35''3'T"2771"" 11.4 "2'27'2 12.3 14.7 10 4 | 10.3 | 6.0 | 1.7

[0024] Using the spreadsheet, month by month, for each of the parameters rainfall, sunshine, minimum and maximum temperatures, number of rainy days greater than 1mm, number of rainy days less than or equal to 1mm, for the year compared (here in the example 2022), it is necessary to reformat in an identical format according to the two corresponding 6-month periods going from January to June of a year n (here in the example 2014) and the second period going from July to December of the year n+1 (here in the example 2015)

[0025] [Tables4] 20U 2Q14 2014 2014 2014 2014 SOIS -2015 2915 2015 2015 2015 Ssfrrt Ôuenf / n. / toupy | 02 d. ■ 1O: SEVEN wi NOV {mfpj j 48.8 23.4 § 23.7 """■""i............. W6.9^ 81.7 30.1 i 59.0 79.0 45.3 67.6 23.7 i4 15 5 | 5 11 8 9 I 9 10 8 13 iiiiiiiiiiI: ' 3 6 $ 7 B 6 6 6 7 11 10 9 ; 12.5 11.3 3.3 3.4 2.9 § 6.3 8.3 11.0 13.4 13.8 9.0 7.0 6.9 6.3 ènss^itefnew «h! [ 46. 1 "ST "sTT [1S8.6 202.2 "~2ÏO3~ 217.0 5 “ïmJ” ^97^ OT.b 63.1

[0026] Using the spreadsheet, month by month, for each of the parameters rainfall, sunshine, minimum and maximum temperatures, number of rainy days greater than 1mm, number of rainy days less than or equal to 1mm, the two tables can be formatted into a single table.

[0027] [Tables5] Suint Quentin, Roupy 02 JAN MAR «R. ■JULY: AUUT' OCT ti&J 2022 45.3, 67.6 £3.7 MS pKiss games > Tmm 2022 B 5 S 5 5 9 2 3 13 7 10 S î5 5 S n 8 S 10 8 13 7 îS $ b 7 S 3 3 8 2 1 8 2 7 11 10 9 Average . n ma xi 2023 60 10.1 13.7 15.3 29.9 23.7 26.8 28.0 20.6 18.6 11.6 6.5 : -l 3.7 20074 / 2$? 5\\ ae 13.3 13.0 is.o 22.0 25.2 23.8' 18.3 14.2 12.5 11.3 Average mini 20S2 2.9 2.9 3.1 4.9 j 9.1 10.4 12.3 1.6 14.7 : 3.3 3.4 53 as 11.0 13.4 13# 941 7.0 8.9 6.3 CRSQItb 1 «i.8 218.6 23B.3 227.2 S 324.1 287.S 165.4 4941.9 W 18816 202.2 253.6 210.3 317.0 Î6A5 978 52.5 88.1

[0028] For the rainfall parameter, the 20-year rainfall normal from 2003 to 2022 and its 20-year standard deviation from 2003 to 2022 are calculated monthly. For each month, the 20-year rainfall normal is added to the standard deviation to give the upper value. The 20-year rainfall normal is also subtracted from the standard deviation to give the lower value. Example for the Météo France station in Roupy, 02

[0029] [Tableauxô] Normal values ​​20 years Standard deviation 20 years High value Low value Jan 56.5 Mo .............SS............. 30 i Feb ...............w...............~ ..................27.4................. .............7771............. 2Z3 Mar ' Apr" 47.3 23.5 7ÜJ .............'3'0............. .. May 59.4 .................................... "...........SO'........... Jun 627 3KB' W1.5 23.W 58.2 2é.3 iS&ë 313 Aug .................67.4............... 41.5 108.9 25K Sep 45.1 21.7 ............SO 23.4 bel 56.0 26.0 84.0 31.9 ................50............... .................22.5 ...........W4........... ....................... from 83.1 31.9 95.1 3L2

[0030] For the cumulative rainfall parameter, each month of the two combined semesters is compared to the high and low values ​​of the 20-day averages for its corresponding month. If the cumulative rainfall value for the month is below the low value, this indicates an exceptionally dry month. If the cumulative rainfall value for the month is above the high value, this indicates an exceptionally wet month. Consequently, the cumulative rainfall value for this outlier month is highlighted (with a change of color, in red in the example).

[0031] These out-of-the-ordinary values ​​are included in a correction system for the reconstructed years.

[0032] Keeping a margin of error of 1mm due to rounding, each value will be corrected by reversing the cumulative rainfall of the month with that of the following month once out of two except in two cases: if the value of the immediately following month is an inverse out-of-standard value, this cancels the reversal while counting in the reversals (so-called compensation effect); if the immediately following value is an out-of-standard value of the same category a new reversal with the following value is carried out and so on while counting as only one reversal (so-called snowball effect).

[0033] This correction system is followed from one year to the next and is carried out for all years.

[0034] The first correction for the year 2016, explained by the first half of 2002 and the second half of 2003 (the reference year), begins with a non-inversion between February and March 2002. This correction system is then applied backward for the years preceding 2016 and continued forward for the years following 2016. The corrections are formatted in the line labeled "corrected precipitation (mm)". Example for the reconstructed year 2014 / 2015.

[0035] [Tables?] Saint Quentin, Raupy U3 t JAN j FEB •MAR- .■ AVF ï-éAt-1 .JUNE. qer §.N0¥ t 60.4 J 43,& £022 j 13.4 27.7 31.9 62.5 11.5 118 8-6.6 34.6 | 54.1 | 46.5 ^8 a?. 4 23; 7¢1.7 50 70 45. 3 | 515 07 ] J 23.4 P3 7 lî." | szl ?s ? .... ... § h&iv* T\[Æ ' * Dp? en, •• ' ah | '■fiormate 30 ysteu? haute J -11? | es.3 typé" JJ -lé — ,<3. 1 14.3 zESK •54.5 ? -44,0 i' / v 1 ' ! ■!>,£■ $ ?'HJ :4:5. / -31?J -7^ | -7L4 "nvEnate 20 ans -î 1 -313 | -26.5 dateur fnûis | -11 •57.3 • 1.14 $ -37.35 —— î ite'WSSéU J-----------'----------

[0036] Following the completion of this table and in order to make it more visual, the final table for each year and each location is presentable in the form of a graph.

[0037] [Tables8] ms

Claims

Claims

1. Meteorological model characterized in the creation of a meteorological tool for the French territory in which a year "n" between 2003 and 2030 is explained by two past half-years located between 1976 and 2030 as presented in [Tables 2] of paragraph [0017] allowing on the one hand the explanation of the rainy or dry weather trends by month of this year "n" between 2003 and 2030 and on the other hand allowing the anticipation one year in advance of the rainy or dry weather trends by month of this year "n" until 2030.

2. Meteorological model in accordance with claim number 1 characterized in the creation of decision support tools making it possible to anticipate one year in advance on winter soft wheat a significant development of one or more phytophagous fungi (yellow rust, brown rust, septoria, microdochium spp), making it possible to anticipate one year in advance on winter barley a significant development of one or more phytophagous fungi (dwarf rust, helminthosporium, ramularia, rynchosporium), making it possible to anticipate one year in advance on rapeseed a significant development of one or more phytophagous fungi (cylindrosporiosis, alternaria, sclerotinia), making it possible to anticipate one year in advance on sugar beet a significant development of one or more phytophagous fungi (cercosporium, ramularia, rust, powdery mildew) and finally making it possible to anticipate one year in advance on the advance on potato a significant development of one or more phytophagous fungi (mildew, alternaria).

3. Meteorological model in accordance with claim number 1 characterized in the creation of meteorological tools making it possible to precisely calculate the climate change of a territory for the parameters rainfall, number of rainy days greater than 1 mm, number of rainy days less than or equal to 1 mm, duration of sunshine, minimum temperatures and maximum temperatures due to the comparison made possible by the tool as presented in [Tables 2] of paragraph [0017] between a year "n" located between 2003 and 2030 and its two past half years located between 1976 and 2030.

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