JPH09304550A - Prediction method for ground surface temperature - Google Patents
Prediction method for ground surface temperatureInfo
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- JPH09304550A JPH09304550A JP11887396A JP11887396A JPH09304550A JP H09304550 A JPH09304550 A JP H09304550A JP 11887396 A JP11887396 A JP 11887396A JP 11887396 A JP11887396 A JP 11887396A JP H09304550 A JPH09304550 A JP H09304550A
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- 238000000034 method Methods 0.000 title claims abstract description 58
- 230000000694 effects Effects 0.000 claims abstract description 7
- 238000001556 precipitation Methods 0.000 claims description 4
- 230000005855 radiation Effects 0.000 claims description 4
- 230000015572 biosynthetic process Effects 0.000 claims description 2
- 230000005457 Black-body radiation Effects 0.000 claims 1
- 238000010586 diagram Methods 0.000 description 8
- 238000005259 measurement Methods 0.000 description 8
- 239000011159 matrix material Substances 0.000 description 7
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Chemical compound O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 4
- 238000007796 conventional method Methods 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 238000009833 condensation Methods 0.000 description 2
- 230000005494 condensation Effects 0.000 description 2
- 230000008014 freezing Effects 0.000 description 2
- 238000007710 freezing Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 1
- 239000010426 asphalt Substances 0.000 description 1
- 210000002747 omentum Anatomy 0.000 description 1
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Abstract
Description
【０００１】[0001]
【発明の属する技術分野】本発明は、地表面温度の予測
方法並びに地表付近の気温及び湿度と地表面温度に基づ
く霜発生の予測方法に関するものである。BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method of predicting a ground surface temperature and a method of predicting frost formation based on the temperature and humidity near the ground surface and the ground surface temperature.
【０００２】[0002]
【従来の技術】路面凍結予測を行う際の直接的な気象要
素は、路面温度と路上水分である。その中の、路面温度
の予測装置の公知文献としては、例えば特公平６−１０
０６６２号公報に示されたものがある。図４は上記公報
に示された従来の路面温度予測装置の構成を説明する図
である。図４において、４１は路面温度予測装置、４２
は気温測定器、４３は風向測定器、４４は風速測定器、
４５は降水量測定器、４６は路面温度測定器であり、路
面の下方でその路面の近傍に埋設され、路面の温度を測
定する。４７は地中温度測定器であり、路面の下方で路
面温度測定器４６よりも下方に埋設され、地中の温度を
測定する。４８は高層気象データ導出手段、４９は中央
処理装置、５０は記憶装置、５２は表示装置、５３は熱
収支量測定装置、６１は温度差測定ユニット、６５は演
算装置である。2. Description of the Related Art The direct meteorological factors when predicting road surface freezing are road surface temperature and road moisture. As a known document of the road surface temperature predicting device, for example, Japanese Patent Publication No. 6-10
There is one disclosed in Japanese Patent No. 0662. FIG. 4 is a diagram for explaining the configuration of the conventional road surface temperature prediction device shown in the above publication. In FIG. 4, 41 is a road surface temperature predicting device, 42
Is an air temperature measuring device, 43 is a wind direction measuring device, 44 is a wind speed measuring device,
Reference numeral 45 is a precipitation measuring device, and 46 is a road surface temperature measuring device, which is buried below the road surface and near the road surface and measures the temperature of the road surface. Reference numeral 47 is an underground temperature measuring device, which is buried below the road surface temperature measuring device 46 below the road surface and measures the underground temperature. 48 is a high-rise meteorological data deriving means, 49 is a central processing unit, 50 is a storage device, 52 is a display device, 53 is a heat balance measuring device, 61 is a temperature difference measuring unit, and 65 is a computing device.
【０００３】図４に示された路面温度予測方式では、中
央処理装置４９が、路面温度の予測を行うために、気
温、風向、風速、降水量、路面温度及び地中温度の各デ
ータＤＴ1 〜ＤＴ6 と、路面温度測定器４６と地中温度
測定器４７との埋設深さの差とその温度差から演算装置
６５が求めた熱収支の測定データＤＴ7 と、気象庁の高
層気象データＤＴ8 〜ＤＴn とを入力し、またこれらの
各データＤＴi に対応して記憶装置５０にストアされて
いる係数値Ｂijを用い、線形重回帰法の演算手法によっ
て路面温度の予測値を算出する。In the road surface temperature predicting method shown in FIG. 4, the central processing unit 49 predicts the road surface temperature in order to predict the road surface temperature. Therefore, each data DT 1 of air temperature, wind direction, wind speed, precipitation amount, road surface temperature and underground temperature is obtained. ~ DT 6 , the difference in the buried depth between the road surface temperature measuring device 46 and the underground temperature measuring device 47, and the heat balance measurement data DT 7 obtained by the calculation device 65 from the temperature difference, and the high-rise meteorological data DT of the Meteorological Agency. 8 to DT n are input, and the coefficient value B ij stored in the storage device 50 corresponding to each of these data DT i is used to calculate the predicted value of the road surface temperature by the calculation method of the linear multiple regression method. To do.
【０００４】[0004]
【発明が解決しようとする課題】図４に示した従来の路
面温度予測方式では、路面温度と地中温度を用いて熱収
支測定を行っているため、それ以前の方式である「晴
れ、曇り」といった天候パターン分けによる予測方法と
比べて、天候の急変に対処することができる。しかしな
がら、記憶装置５０にストアされている係数の値は過去
の気象データに基づくため、数シーズンのデータの蓄積
が必要である。従って過去に経験した天候の急変に対し
ては予測可能であるが、前例の無い気象状況については
対応できないという問題があった。また熱収支の測定は
局所的であるので、予測対象領域すべての予測を行うに
は、予測対象領域の全ての場所で熱収支を測定する必要
があるという問題もあった。In the conventional road surface temperature prediction method shown in FIG. 4, since the heat balance is measured by using the road surface temperature and the underground temperature, the previous method "sunny, cloudy" is used. It is possible to cope with a sudden change in weather as compared with a prediction method based on weather pattern classification such as. However, since the coefficient value stored in the storage device 50 is based on past weather data, it is necessary to accumulate data for several seasons. Therefore, there is a problem in that it is possible to predict sudden changes in weather experienced in the past, but it is not possible to cope with unprecedented weather conditions. Further, since the measurement of the heat balance is local, there is also a problem that the heat balance needs to be measured at all locations in the prediction target region in order to predict the entire prediction target region.
【０００５】[0005]
【課題を解決するための手段】本発明に係る地表面温度
の予測方法は、気象庁の気象予報数値モデルデータ、気
象観測現場での観測データ及び地形効果のデータを局地
気象モデルに供給し、該局地気象モデルは供給されたデ
ータを所望の気象を表す方程式に代入し、該方程式から
拡張カルマンフィルタ法により路面温度予測に必要な各
所の各予測時刻の気象データを算出して路面温度推定モ
デルに供給し、該路面温度推定モデルは供給された気象
データを用いて熱収支法に基づき路面温度を推定するも
のである。上記の予測方法によって、従来方法では不可
能であった、測定データのない場所や少ない場所での予
測及び過去に経験したことのない気象状況での予測を、
気象学的知識を基に行うことが可能である。また、局所
的な路面温度の分布を求めることも可能である。The method for predicting the ground surface temperature according to the present invention provides a weather forecast numerical model data of the Japan Meteorological Agency, observation data at a weather observation site, and topographic effect data to a local weather model, The local meteorological model substitutes the supplied data into an equation representing a desired meteorology, and calculates the meteorological data at each predicted time of each place necessary for the road surface temperature prediction by the extended Kalman filter method from the equation to calculate the road surface temperature estimation model. The road surface temperature estimation model estimates the road surface temperature based on the heat balance method using the supplied meteorological data. With the above prediction method, it is impossible with the conventional method to make predictions in places where there is no measurement data or in places where there is little measurement data, and in weather conditions that have not been experienced in the past.
It can be done based on meteorological knowledge. It is also possible to obtain a local distribution of road surface temperature.
【０００６】[0006]
実施形態１．図１は本発明の実施形態１に係る路面温度
予測方法の説明図である。本実施形態１では、従来技術
の手法である重回帰法などの統計的手法を用いる代わり
に、気象学的知識に基づき、測定データの無い地点での
気象値の予測を行い、熱収支法による表面温度の推定を
行う方法を採用している。図１においては、路面温度予
測（推定）に下記の３種類のデータを用いる。 （１）気象庁のＧＰＶ（Gird Point Value：気象庁の数
値モデルのデータで、例えば降水量、気温、風向、風
速、露点温度など） （２）現場での観測値（例えば気温、風向、風速、路面
温度など） （３）地形効果データ（例えば山、丘陵、河川、湖沼、
建築物など）Embodiment 1 FIG. FIG. 1 is an explanatory diagram of a road surface temperature prediction method according to the first embodiment of the present invention. In the first embodiment, instead of using a statistical method such as a multiple regression method, which is a method of the related art, a meteorological knowledge is used to predict a meteorological value at a point where there is no measurement data, and the heat balance method is used. The method of estimating the surface temperature is adopted. In FIG. 1, the following three types of data are used for road surface temperature prediction (estimation). (1) Meteorological Agency's GPV (Gird Point Value: numerical model data of the Meteorological Agency, for example, precipitation, temperature, wind direction, wind speed, dew point temperature, etc.) (2) Field observation values (eg temperature, wind direction, wind speed, road surface) (3) Topographic effect data (eg mountains, hills, rivers, lakes,
Buildings etc.)
【０００７】上記３種類のデータは、平均値データであ
るか、または対象領域（空間）のごく一部の空間のデー
タである。従って路面温度の空間分布を求めるために、
これらのデータを局地気象モデルに供給する。次に局地
気象モデルでは、これらのデータを気象を表す方程式に
代入し、この方程式から拡張カルマンフィルタ法（ＥＫ
Ｆ法）により、路面温度推定で必要な各所の各予測時刻
の気象値を算出する。次に上記各所の各予測時刻の気象
値を用いて、熱収支法に基づく路面温度推定により、路
面温度を推定する。上記が実施形態１における基本的な
路面温度予測方法である。The above three types of data are average value data or data of a small part of the target area (space). Therefore, in order to obtain the spatial distribution of road surface temperature,
These data are supplied to the local weather model. Next, in the local meteorological model, these data are substituted into an equation representing the meteorology, and the extended Kalman filter method (EK
The F method) is used to calculate the meteorological value at each predicted time at each location required for road surface temperature estimation. Next, the road surface temperature is estimated by the road surface temperature estimation based on the heat balance method using the meteorological value at each predicted time of each place. The above is the basic road surface temperature prediction method in the first embodiment.
【０００８】以下図１の各処理を詳しく説明する。図１
の局地気象モデルブロックは少ないデータから、各所の
各時刻の気象値の予測を行い、この予測気象値に基づき
路面温度推定ブロックで路面温度の推定を行う。局地気
象モデルブロックに入力される気象庁のＧＰＶは、３時
間おきの２４時間先までの予報値が提供される。そこ
で、各３時間の間の気象値の推定を、局地気象モデル内
で行う。局地気象モデルブロックは、気象を表す方程式
系と、これらの方程式を解く拡張カルマンフィルタ（Ｅ
ＫＦ）から構成される。ここで、拡張カルマンフィルタ
とは、本来線形現象を対象としているカルマンフィルタ
を非線形現象に拡張したものを指す。熱収支では、気
温、風向、風速及び湿度のデータが必要なため、大気の
連続方程式（１）、熱力学第１法則の方程式（２）、運
動方程式（３）及び比湿の保存則の方程式（４）が必要
である。以下に上記方程式（１）〜（４）を具体的に示
す。Each processing of FIG. 1 will be described in detail below. FIG.
The local meteorological model block of 3) predicts the weather value at each time at each place from a small amount of data, and the road surface temperature estimation block estimates the road surface temperature based on the predicted meteorological value. The Meteorological Agency's GPV input to the local weather model block is provided with forecast values every 3 hours up to 24 hours ahead. Therefore, the estimation of the meteorological value for each 3 hours is performed in the local meteorological model. The local weather model block includes a system of equations representing weather and an extended Kalman filter (E) that solves these equations.
KF). Here, the extended Kalman filter refers to an extension of the Kalman filter originally intended for a linear phenomenon to a non-linear phenomenon. In the heat balance, data of temperature, wind direction, wind speed and humidity are necessary, so the continuous equation of the atmosphere (1), the equation of the first law of thermodynamics (2), the equation of motion (3) and the equation of conservation of specific humidity (4) is required. The above equations (1) to (4) are specifically shown below.
【０００９】[0009]
【数１】 [Equation 1]
【００１０】ただし、ここで、ｉ，ｊ，ｋは方位を表
し、ρは密度、θは温位、ｕは速度、ｐは圧力、Ωはコ
リオリパラメータ、ｑは比湿（水蒸気の密度／湿潤空気
の密度）である。そして、Ｓθ，Ｓｑはそれぞれ、θ，
ｑのソースを表す。なお、ｘｊは３つの座標成分を１つ
の方式で表すときに使う便法で、ｊ＝１の時ｘ１＝ｘ
（東西方向）、ｊ＝２の時ｘ２＝ｙ（南北方向）、ｊ＝
３の時ｘ３＝ｚ（鉛直方向）を表す。式（３）の−ｇδ
i3の意味について説明すると、ｇは重力加速度を表し、
δijはクロネッカーのデルタという数学の記号で、ｉと
ｊが等しいときには１、等しくないときには０になる。
この場合のδi3については、１は東西方向、２は南北方
向、３は鉛直方向を表し、ｉが３すなわち式（３）が鉛
直方向を表すときのみ−ｇδi3の項があることになる。
またεijk は次の式（５）で示される。Here, i, j, and k represent azimuths, ρ is density, θ is temperature, u is velocity, p is pressure, Ω is Coriolis parameter, and q is specific humidity (water vapor density / wetness). Density of air). Then, S θ and S q are θ and
Represents the source of q. Note that xj is the expedient used when expressing three coordinate components by one method, and when j = 1, x1 = x
(East-west direction), when j = 2, x2 = y (south-north direction), j =
When 3, it represents x3 = z (vertical direction). -Gδ in equation (3)
Explaining the meaning of i3 , g represents gravitational acceleration,
δ ij is a Kronecker delta mathematical symbol, which is 1 when i and j are equal and 0 when they are not equal.
Regarding δ i3 in this case, 1 represents the east-west direction, 2 represents the north-south direction, 3 represents the vertical direction, and there is a term of −gδ i3 only when i is 3, that is, when the formula (3) represents the vertical direction. .
Further, ε ijk is expressed by the following equation (5).
【００１１】[0011]
【数２】 [Equation 2]
【００１２】ここで、odd permutation は奇置換、even
permutaion は偶置換を表す。そして置換を奇数回行っ
た場合を奇置換、偶数回行った場合を偶置換という。こ
れを具体的に説明すると、 ε123 ＝１ これに対して、インデックスの１番目と２番目を入れ替
えた場合（１回置換を行った場合）は、 ε213 ＝−１ さらに２番目と３番目を入れ替えた場合（２回置換を行
った場合）は、 ε231 ＝１ になる。温位θは、仮温度Ｔv 、圧力ｐを用いて以下の
式（６）のように表される。Where odd permutation is an odd permutation, even
permutaion represents even permutation. The case where the replacement is performed an odd number of times is called odd replacement, and the case where the replacement is performed an even number of times is called even replacement. More specifically, ε 123 = 1 On the other hand, when the first index and the second index are exchanged (when the replacement is performed once), ε 213 = -1 and the second index and the third index When is replaced (when replacement is performed twice), ε 231 = 1. The temperature θ is represented by the following equation (6) using the temporary temperature T v and the pressure p.
【００１３】[0013]
【数３】 (Equation 3)
【００１４】ここで、Ｒd は乾燥空気の気体定数、Ｃp
は定圧比熱、ｈＰａはパスカル単位で表した大気圧であ
る。仮温度は気体の状態方程式及び比湿との関係式から
以下の式（７），（８）のように表される。 ｐ＝ρＲd Ｔv ……（７） Ｔv ＝Ｔ（１＋０．６ｌｑ） ……（８） これらの方程式は、目的の気象値が含まれるように適宜
取捨選択・変形をする必要がある。カルマンフィルタ法
を用いる場合には、差分化をする必要がある。この差分
化には様々な方法があるが、３次元運動の際のエネルギ
ー及び２次元運動の際のエンストロフィーを保存する差
分化法としては、Yoshizaki の方法“J.Meteorel.Soc.
Jpn., 63[3],June 1985, M.Yoshizaki, A New Second-O
rder Finite-Difference form of Three-Dimensional M
omentum Equations in the Anelastic System, p.397〜
404"がある。Where R d is the gas constant of dry air, C p
Is the specific heat at constant pressure, and hPa is the atmospheric pressure expressed in Pascal unit. The temporary temperature is expressed by the following equations (7) and (8) from the equation of state of gas and the relational equation with specific humidity. p = ρR d T v (7) T v = T (1 + 0.6lq) (8) These equations need to be appropriately selected and modified so that the target meteorological value is included. When using the Kalman filter method, it is necessary to make a difference. There are various methods for this difference, but Yoshizaki's method “J. Meteorel. Soc.” Is used as a difference method for preserving energy during three-dimensional motion and enstrophy during two-dimensional motion.
Jpn., 63 [3], June 1985, M. Yoshizaki, A New Second-O
rder Finite-Difference form of Three-Dimensional M
omentum Equations in the Anelastic System, p.397 ~
There is a 404 ".
【００１５】カルマンフィルタ法は、少ない観測データ
から、各所での値の推定値を与える方法である。以下に
カルマンフィルタの概略と、本発明での適用方法を示
す。カルマンフィルタ法では、系を記述する方程式系及
び観測結果にガウス性のノイズが含まれていると考え、
系の時間発展の記述を以下の行列の差分式（９），（１
０）で行う。なお式（９）は状態方程式、式（１０）は
観測方程式についての行列の差分式である。 ｘk+1 ＝Ｆk ｘk ＋Ｇk ｗk ……（９） ｙk ＝Ｈk ｘk ＋ｖk ……（１０）The Kalman filter method is a method of giving an estimated value at each place from a small amount of observation data. The outline of the Kalman filter and the application method of the present invention are shown below. In the Kalman filter method, it is considered that Gaussian noise is included in the equation system that describes the system and the observation result,
A description of the time evolution of the system is given by the following matrix difference equations (9), (1
0). The equation (9) is a state equation, and the equation (10) is a matrix difference equation for the observation equation. x k + 1 = F k x k + G k w k (9) y k = H k x k + v k (10)
【００１６】ここで、ｋは時間経過を表すものである
が、離散的になっている。（即ち連続的なものではな
く、例えば、ｋの示す変数が６時００分での状態を表す
とすると、ｋ＋１の示す変数は６時０１分の状態を表す
と言った形式である）そしてこれらは行列形式で書かれ
る。またここで、ｘk は状態ベクトル、ｙk は観測信
号、ｗk はシステムノイズ、ｖk は観測ノイズである。
すなわち、状態ベクトルｘk は推定したい気温・風速等
の気象値の誤差を含んだ値を表す。Ｇk ｗk は系のノイ
ズを表す。Ｆk は系の時間発展を表し、前記式（１）〜
（４）を差分化した漸化式が、次の式（９′）となるよ
うにＦk を決定する。 ｘk+1 ＝Ｆk ｘk ……（９′）Here, k represents the passage of time, but is discrete. (That is, it is not continuous, for example, if the variable indicated by k represents the state at 6:00, the variable indicated by k + 1 represents the state at 6: 1) and these Is written in matrix form. Here, x k is a state vector, y k is an observation signal, w k is system noise, and v k is observation noise.
That is, the state vector x k represents a value including an error in the meteorological value such as the temperature and wind speed to be estimated. G k w k represents system noise. F k represents the time evolution of the system and is expressed by the above equations (1) to
F k is determined so that the recurrence formula obtained by differentiating (4) becomes the following formula (9 ′). x k + 1 = F k x k (9 ')
【００１７】各種観測データは、観測信号ｙk に対応す
る。例えば、ＧＰＶの値は平均値を表している。ｙk 1が
状態ベクトルｘk 1〜ｘk iの平均とすると、ｙk 1＝（ｘk 1
＋ｘk 2＋…＋ｘk i）／ｉとなる。この様に観測信号値ｙ
k と状態ベクトルｘk との関係を考慮して式（１０）の
Ｈk を決定する。なおここで、上記ｙk iのｉは、行列内
の値を表すものであり、いまｙk の要素が３個だとする
と、具体的には、ｙk は、ｙk 1，ｙk 2，ｙk 3の行列を意
味する。行列の大きさは、観測データ数をｍ個、推定気
象値の数をｎ個とすると、状態ベクトルｘk は１×ｎ、
Ｆk はｎ×ｎ、観測信号ｙk は１×ｍ、Ｈk はｍ×ｎ、
観測ノズルｖk は１×ｍである。状態ベクトルｘk の値
自体は誤差を含むので、ｘk の最小分散推定を行うと以
下の漸化式（１１）〜（１７）よりｘk の期待値（陣笠
記号の付加されたもの）が求められる。The various observation data correspond to the observation signal y k . For example, the value of GPV represents the average value. When y k 1 is the average of the state vector x k 1 ~x k i, y k 1 = (x k 1
+ X k 2 + ... + x k i ) / i. In this way, the observed signal value y
H k of the equation (10) is determined in consideration of the relationship between k and the state vector x k . Here, i of y k i represents a value in a matrix, and if y k has three elements, specifically, y k is y k 1 , y k 2 , y. It means a matrix of k 3 . The size of the matrix is such that the state vector x k is 1 × n, where m is the number of observation data and n is the number of estimated weather values.
F k is n × n, observed signal y k is 1 × m, H k is m × n,
The observation nozzle v k is 1 × m. Since the value itself of the state vector x k includes an error, when the minimum variance estimation of x k is performed, the expected value of x k (with the Jinkasa sign) is obtained from the following recurrence formulas (11) to (17). Desired.
【００１８】[0018]
【数４】 (Equation 4)
【００１９】ここで、陣笠記号の付加された期待値を求
める場合のｋとｋ、又はｋと（ｋ＋１）との間の縦棒
は、条件付き確率を表すときに用いるのと同じ記号で、
ｘk|kの期待値は時刻ｋでの状態が判ったときの時刻ｋ
でのｘの期待値、ｘk+1|k の期待値は時刻ｋでの状態が
判ったときの時刻ｋ＋１でのｘの期待値になる。また、
Ｈk の左肩にｔを付けた tＨk は、Ｈk の転地行列を表
す。また式、（１６），（１７）のｘ0|-1は、ｘk+1|k
のｋ＝−１の場合を表し、ｘ0 の上に水平の棒は初期値
ｘ0 の平均を表し、Σｘ0 は初期値ｘ0 の分散を表す。Here, the vertical bar between k and k or k and (k + 1) in the case of obtaining the expected value with the Jinkasa symbol is the same symbol used when expressing the conditional probability,
The expected value of x k | k is time k when the state at time k is known.
The expected value of x at x and the expected value of x k + 1 | k become the expected value of x at time k + 1 when the state at time k is known. Also,
T H k which gave a t the left shoulder of the H k represents the change of air matrix H k. Further, x 0 | -1 in the equations (16) and (17) is x k + 1 | k
Represents a case of k = -1, the horizontal bar on the x 0 represents the average of the initial value x 0,? X 0 represents the variance of the initial value x 0.
【００２０】この手法から、熱収支に必要な各種気象値
の期待値を求めることができる。道路の素材にコンクリ
ート・アスファルトを使用した場合を考えると、これら
の素材は熱伝導が悪い。従って、水平方向の熱移動は遅
く、鉛直方向の熱収支の状態で路面温度が決定される。
地表面での熱収支は以下の式（１８）で表される。From this method, expected values of various meteorological values necessary for heat balance can be obtained. Considering the case where concrete asphalt is used for road materials, these materials have poor heat conduction. Therefore, the heat transfer in the horizontal direction is slow, and the road surface temperature is determined in the state of the heat balance in the vertical direction.
The heat balance on the ground surface is expressed by the following equation (18).
【００２１】[0021]
【数５】 (Equation 5)
【００２２】ここで式（１８）の左辺の入力輻射は、太
陽から直接或いは間接的に入射される輻射により、太陽
と対象地域の相対位置、空気中の水分量（雲も当然含め
る）等から算出されるもので、基本的には気象庁のデー
タなどから算出できるものである。また式（１８）の右
辺の１項目は表面温度そのもの、２・３項目はバルク式
を用いると温度に関する式が求められる。４項目は地表
面での温度勾配になる。従って、式（１８）は、右辺が
温度に対する微分方程式、左辺がソース項になる。この
微分方程式に必要な各パラメータをカルマンフィルタ法
を用いて求めることになる。Here, the input radiation on the left side of the equation (18) depends on the relative position between the sun and the target area, the amount of water in the air (including clouds, of course), etc. due to the radiation directly or indirectly incident from the sun. It is calculated and basically can be calculated from the data of the Japan Meteorological Agency. In addition, one item on the right side of the equation (18) is the surface temperature itself, and the other two items are the bulk equations, and the equation relating to the temperature is obtained. The fourth item is the temperature gradient on the ground surface. Therefore, in the equation (18), the right side is a differential equation with respect to temperature, and the left side is a source term. Each parameter required for this differential equation will be obtained by using the Kalman filter method.
【００２３】図２は本発明に係る熱収支法を用いた路面
温度の計算に必要な気象データを示す図である。ここ
で、時間に依存しない、アルベド・バルク輸送係数や地
中の熱伝導度などは、地形などを考慮し局地気象モデル
とは別に求める。入力輻射に関しては、水蒸気量、気温
及び気圧の値が必要で、顕熱及び潜熱に関しては風速、
気温及び比湿の値が必要であり、これらの値は局地気象
モデルより求められる。また、地中伝導熱に関しては地
中での熱伝導方程式を用いる。以上の熱収支により路面
温度の予測値を求めることができる。FIG. 2 is a diagram showing the meteorological data necessary for calculating the road surface temperature using the heat balance method according to the present invention. Here, the albedo-bulk transport coefficient and the thermal conductivity in the ground, which do not depend on time, are obtained separately from the local weather model in consideration of the topography. For input radiation, the amount of water vapor, temperature and atmospheric pressure are required, for sensible heat and latent heat, wind speed,
Temperature and specific humidity values are required and these values are obtained from the local weather model. For underground heat, the heat conduction equation in the ground is used. The predicted value of the road surface temperature can be obtained from the above heat balance.
【００２４】本実施形態１によって、従来方法では不可
能であった、測定データのない場所や少ない場所での予
測及び過去に経験したことのない気象状況での予測を、
気象学的知識を基に行うことが可能である。また、局所
的な路面温度の分布を求めることも可能である。According to the first embodiment, it is possible to perform a prediction in a place where there is no measurement data or in a place where there is little measurement data and a prediction in a meteorological condition that has not been experienced in the past, which is impossible with the conventional method.
It can be done based on meteorological knowledge. It is also possible to obtain a local distribution of road surface temperature.
【００２５】実施形態２．図３は本発明の実施形態２に
係る霜予測方法の説明図である。前記実施形態１の方法
は、農業気象の霜予測に応用することができる。一般に
農地では、気象ロボットが各所に配置されているので、
これを現場での観測値として有効活用できる。即ち局地
気象モデルにより求められた地表面付近の気温・湿度及
び熱収支法による地表面温度の推定より、霜予測を行う
ことができる。基本的な予測方法は、実施形態１と同様
である。霜予測に際しては、水蒸気の結露・凝結が問題
となる。地表付近の湿度と気温及び地表面温度が推定で
きると、蒸気圧曲線などから霜の発生の判定ができる。
地表付近の湿度と気温に関しては局地気象モデルブロッ
クが直接推定し、また、地表面温度に関しては実施形態
１の路面温度推定と同一方法の地表面温度推定ブロック
により推定できる。霜予測ブロックは、前記地表付近の
湿度及び気温と地表面温度に基づき霜の発生を予測す
る。Embodiment 2. FIG. 3 is an explanatory diagram of a frost prediction method according to the second embodiment of the present invention. The method of the first embodiment can be applied to frost prediction of agricultural weather. Generally, in farmland, there are meteorological robots in various places,
This can be effectively used as an on-site observation value. That is, frost prediction can be performed by estimating the temperature and humidity near the ground surface obtained by the local weather model and the ground surface temperature by the heat balance method. The basic prediction method is the same as in the first embodiment. Condensation and condensation of water vapor become a problem when predicting frost. When the humidity, air temperature, and surface temperature near the surface of the earth can be estimated, it is possible to determine the occurrence of frost from the vapor pressure curve or the like.
The humidity and temperature near the surface of the earth can be estimated directly by the local weather model block, and the surface temperature can be estimated by the surface temperature estimation block of the same method as the road surface temperature estimation of the first embodiment. The frost prediction block predicts the occurrence of frost based on the humidity and air temperature near the ground surface and the ground surface temperature.
【００２６】本実施形態１，２により、測定点以外での
予測及び過去に経験したことのない気象状況での予測
を、気象学的知識を基に行うと共に、非経験的に局所的
な路面温度の分布も求められるので、本発明は、地表面
の熱収支に関係する各種予測に利用できる。例えば、路
面凍結予報を行う際の路面温度の予測或いは、農業気象
の霜予報を行う際の地表面温度・地表付近の気温・湿度
の予測に用いることができる。According to the first and second embodiments, predictions other than the measurement points and predictions in a meteorological condition that have not been experienced in the past are performed based on the meteorological knowledge, and a non-empirical local road surface is obtained. Since the temperature distribution is also obtained, the present invention can be used for various predictions related to the heat balance of the ground surface. For example, it can be used for predicting the road surface temperature when making a road surface freezing forecast, or for making predictions of the ground surface temperature and the temperature and humidity near the ground surface when making a frost forecast of agricultural weather.
【００２７】[0027]
【発明の効果】以上のように本発明によれば、気象庁の
気象予報数値モデルデータ、気象観測現場での観測デー
タ及び地形効果のデータを局地気象モデルに供給し、該
局地気象モデルは供給されたデータを所望の気象を表す
方程式に代入し、該方程式から拡張カルマンフィルタ法
により路面温度予測に必要な各所の各予測時刻の気象デ
ータを算出して路面温度推定モデルに供給し、該路面温
度推定モデルは供給された気象データを用いて熱収支法
に基づき路面温度を推定するようにしたので従来方法で
は不可能であった、測定データのない場所や少ない場所
での予測及び過去に経験したことのない気象状況での予
測を、気象学的知識を基に行うことが可能となった。ま
た、局所的な路面温度の分布を求めることも可能となっ
た。As described above, according to the present invention, the weather forecast numerical model data of the Meteorological Agency, the observation data at the meteorological observation site, and the data of the terrain effect are supplied to the local weather model, and the local weather model is Substitute the supplied data into the equation expressing the desired weather, calculate the meteorological data at each predicted time required for road surface temperature prediction by the extended Kalman filter method from the equation, and supply it to the road surface temperature estimation model. Since the temperature estimation model uses the supplied meteorological data to estimate the road surface temperature based on the heat balance method, it is impossible with the conventional method. It has become possible to make forecasts in meteorological situations that have never been done based on meteorological knowledge. It also became possible to obtain the local distribution of road surface temperature.
【図１】本発明の実施形態１に係る路面温度予測方法の
説明図である。FIG. 1 is an explanatory diagram of a road surface temperature prediction method according to a first embodiment of the present invention.
【図２】本発明に係る熱収支法を用いた路面温度の計算
に必要な気象データを示す図である。FIG. 2 is a diagram showing meteorological data necessary for calculating road surface temperature using the heat balance method according to the present invention.
【図３】本発明の実施形態２に係る霜予測方法の説明図
である。FIG. 3 is an explanatory diagram of a frost prediction method according to the second embodiment of the present invention.
【図４】従来の路面温度予測装置の構成を説明する図で
ある。FIG. 4 is a diagram illustrating a configuration of a conventional road surface temperature prediction device.
Claims (4)
象観測現場での観測データ及び地形効果のデータを局地
気象モデルに供給し、該局地気象モデルは供給されたデ
ータを所望の気象を表す方程式に代入し、該方程式から
拡張カルマンフィルタ法により路面温度予測に必要な各
所の各予測時刻の気象データを算出して路面温度推定モ
デルに供給し、該路面温度推定モデルは供給された気象
データを用いて熱収支法に基づき路面温度を推定するこ
とを特徴とする地表面温度の予測方法。1. The weather forecast numerical model data of the Japan Meteorological Agency, observation data at a weather observation site, and topographic effect data are supplied to a local weather model, and the local weather model represents the desired weather. Substituting into the equation, by the extended Kalman filter method by the Kalman filter method to calculate the meteorological data at each predicted time required for the road surface temperature is supplied to the road surface temperature estimation model, the road surface temperature estimation model the supplied meteorological data A method for predicting ground surface temperature, characterized by estimating road surface temperature based on the heat balance method.
象観測現場での観測データ及び地形効果のデータを局地
気象モデルに供給し、該局地気象モデルは供給されたデ
ータを所望の気象を表す方程式に代入し、該方程式から
拡張カルマンフィルタ法により路面温度予測に必要な各
所の各予測時刻の気象データを算出して路面温度推定モ
デルに供給すると共に、地表付近の気温及び湿度データ
を霜予測モデルに供給し、前記路面温度推定モデルは供
給された気象データを用いて熱収支法に基づき地表面温
度を推定して前記霜予測モデルに供給し、該霜予測モデ
ルは供給された地表付近の気温及び湿度データと地表面
温度に基づき霜の発生を予測することを特徴とする地表
面温度の予測方法。2. The weather forecast numerical model data of the Meteorological Agency, observation data at a weather observation site, and topographic effect data are supplied to a local weather model, and the local weather model represents the desired weather. Substituting into the equation, using the extended Kalman filter method to calculate the meteorological data at each forecast time at each location required for road surface temperature prediction and supplying it to the road surface temperature estimation model, and the temperature and humidity data near the surface of the ground to the frost prediction model. The frost prediction model is supplied to the frost prediction model by estimating the ground surface temperature based on a heat balance method using the supplied meteorological data, and the frost prediction model is supplied with the temperature near the ground surface. And a method for predicting ground surface temperature, which predicts frost formation based on humidity data and ground surface temperature.
は少くとも降水量、気温、風向、風速及び露点温度を含
み、前記気象観測現場での観測データは少くとも気温、
風向、風速及び路面温度を含み、前記地形効果のデータ
は少くとも山、丘陵、河川及び湖沼のデータを含むこと
を特徴とする請求項１又は請求項２記載の地表面温度の
予測方法。3. The weather forecast numerical model data of the Meteorological Agency includes at least precipitation, temperature, wind direction, wind speed and dew point temperature, and the observation data at the meteorological observation site includes at least temperature,
The method for predicting the ground surface temperature according to claim 1 or 2, wherein the method includes the wind direction, the wind speed, and the road surface temperature, and the topographic effect data includes at least data of mountains, hills, rivers, and lakes.
熱、潜熱、地中伝導熱及び太陽からの入力輻射について
の熱収支を算出して路面温度を推定することを特徴とす
る請求項１から請求項３のいずれかに記載の地表面温度
の予測方法。4. The heat balance method estimates a road surface temperature by calculating a heat balance of black body radiation, sensible heat, latent heat, ground conduction heat and input radiation from the sun on the ground surface. The method for predicting the ground surface temperature according to any one of claims 1 to 3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP11887396A JPH09304550A (en) | 1996-05-14 | 1996-05-14 | Prediction method for ground surface temperature |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP11887396A JPH09304550A (en) | 1996-05-14 | 1996-05-14 | Prediction method for ground surface temperature |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| JPH09304550A true JPH09304550A (en) | 1997-11-28 |
Family
ID=14747239
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP11887396A Pending JPH09304550A (en) | 1996-05-14 | 1996-05-14 | Prediction method for ground surface temperature |
Country Status (1)
| Country | Link |
|---|---|
| JP (1) | JPH09304550A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006189403A (en) * | 2005-01-07 | 2006-07-20 | Iwate Prefecture | Frosting prediction device |
| KR20180070959A (en) * | 2016-12-19 | 2018-06-27 | 주식회사 한화 | Method and Apparatus for compensating meterological data |
| KR102402469B1 (en) * | 2021-12-08 | 2022-05-26 | 한국외국어대학교 연구산학협력단 | System and method for predicting road surface temperature of target area using solar radiation data |
| KR102439241B1 (en) * | 2022-03-11 | 2022-09-01 | 주식회사 윈드위시 | Apparatus, Method and System for Predicting Frost Possibility Using Artificial Intelligence Model |
| JP2024029448A (en) * | 2022-08-22 | 2024-03-06 | 株式会社Ihi | Thermal/wind environment information generation device, thermal/wind environment information generation method, disaster prevention/mitigation information generation device, and disaster prevention/mitigation information generation method |
-
1996
- 1996-05-14 JP JP11887396A patent/JPH09304550A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006189403A (en) * | 2005-01-07 | 2006-07-20 | Iwate Prefecture | Frosting prediction device |
| KR20180070959A (en) * | 2016-12-19 | 2018-06-27 | 주식회사 한화 | Method and Apparatus for compensating meterological data |
| KR102402469B1 (en) * | 2021-12-08 | 2022-05-26 | 한국외국어대학교 연구산학협력단 | System and method for predicting road surface temperature of target area using solar radiation data |
| KR102439241B1 (en) * | 2022-03-11 | 2022-09-01 | 주식회사 윈드위시 | Apparatus, Method and System for Predicting Frost Possibility Using Artificial Intelligence Model |
| JP2024029448A (en) * | 2022-08-22 | 2024-03-06 | 株式会社Ihi | Thermal/wind environment information generation device, thermal/wind environment information generation method, disaster prevention/mitigation information generation device, and disaster prevention/mitigation information generation method |
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