# Influence of Meteorological Parameters on the Urban Heat Island in Moscow

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

^{2}. Thus, the population density in “Old Moscow” is approximately 11,280 people per km

^{2}(for comparison, in “New Moscow”, there are only 306 people per km

^{2})

**.**The area of Moscow region, without the traditional area of the city, is approximately 45,800 km

^{2}.

_{2}according to De Martonne classification); the climate of the “temperate forest zone” (according to Berg’s classification). The average annual air temperature in Moscow region, as shown below, ranges from 6 to 7 °C; annual rainfall is approximately 700 mm. It also should be noted that the relief is flat and there are no large open water areas here.

#### 2.2. Data/Materials

^{2}), it means that each weather station covers 8833 km

^{2}on average. However, the recommended optimal density of the meteorological network on the plain is 2500 km

^{2}, because the optimal distance between neighboring stations is 50 km according to Bespalov [21].

^{2}) is 1019 km

^{2}, which is eight times higher than the national average. This allows studying not only regional climate but also even the mesoscale phenomena such as the urban heat island.

_{S}measurements is ±1.0 °C.

#### 2.3. Analysis

## 3. Results and Discussion

#### 3.1. The UHI Intensity in Moscow

_{MAX}), whereas the latter one allows calculating the average UHI intensity over space (ΔT

_{AV}) according to Lokoshchenko [8]:

_{C}is the air temperature at the weather station in the centre of the city, T

_{U}is the air temperature at other urban weather stations, T

_{R}is the air temperature at the rural weather stations, n is the number of stations located on the outskirts of the city, and m is the number of stations located in the rural zone. It is better to use data from manned stations as more reliable; in our case, n = 4 and m = 13. The ΔT

_{MAX}value is larger and, hence, reflects the UHI more clearly. On the other hand, ΔT

_{AV}is more reliable as an average of all urban stations. This parameter is often used in studying the so-called “surface urban heat islands” (SUHI) using radiometric measurements of the surface temperature T

_{S}from satellites. Usually, the SUHI intensity is calculated as the difference between the average values of the T

_{S}in all cells inside the city and in all cells outside it in some comparison zone; for Moscow see, for example, Lokoshchenko and Enukova [23].

_{MAX}and ΔT

_{AV}in Moscow for every 3 h during three years with a total data sampling of 8768 paired values are compared. As is seen from Figure 1, firstly, the statistical relation is close to linear, and secondly, it is very close: the correlation coefficient R is 0.91. It should be noted that the comparison of the mean daily values of ΔT

_{MAX}and ΔT

_{AV}for the same period from 2018 to 2020 with a total sampling of 1096 demonstrated almost the same result: 0.90 in Lokoshchenko and Alekseeva [7]. A small scatter of data is a result of changes in the thermal heterogeneity of Moscow area, i.e., changes in the differences between the data from the central station Balchug and the other four urban stations.

_{S}in Moscow on average for the period of 2008–2015 is 2.6 °C [23].

_{MAX}and ΔT

_{AV}were 7.4 and 4.4 °C, respectively. Late at night (at 3 a.m.), ΔT

_{MAX}and ΔT

_{AV}were even higher: 11.5 and 6.3 °C, respectively, because T was −10.8 °C at the central urban Balchug station; −16.0 °C on average at all five manned city stations; −22.3 °C on average for 13 manned rural stations.

_{MAX}was slightly lower (11.2 °C), while ΔT

_{AV}, on the contrary, became higher than it was 3 h before: 6.7 °C. As can be seen, the T field is more heterogeneous than in Figure 2a due to one-time non-averaged data and, in addition, the specifics of the station location. Unlike Figure 2a, the isotherms in Figure 2b were drawn with a step of 2.0 °C. At Balchug station, T at 6 a.m. was −12.4 °C, while at other urban manned stations, it ranged from −15.9 to −21.0 °C. Thus, as seen in Figure 2b, the nocturnal UHI in Moscow in strong anticyclone conditions was presented by four closed isotherms inside the city (from −14 to −20 °C) and two semi-closed isotherms around it (−22 and −24 °C). Some additional features of the T spatial field, such as a minimum on the east (T was −29.5 °C at the Cherusti station) and a secondary maximum in the north, are probably associated with the influence of relief and other local factors.

#### 3.2. Statistical Analysis of the Influence of Meteorological Parameters on the UHI in Moscow

_{MAX}and ΔT

_{AV}with various meteorological parameters. Eleven parameters were taken into account: total and low cloudiness; four wind parameters (mean daily wind speed, maximal value of wind speed according to eight readings during the day, maximal daily wind gust at a height of 15 m according to M-63 data and mean daily wind speed in the 40–200 m air layer according to MODOS sodar data); daily amplitudes of air and surface temperatures; the highest air pressure, the lowest relative humidity and precipitation amount during the day. Evidently, only cloudiness, wind parameters and possibly precipitation may directly affect the UHI intensity. The T and T

_{S}amplitudes indirectly indicate anticyclonic conditions; relative humidity indirectly reflects changes in T. As we know a priori, the highest air pressure is not a real indicator of anticyclonic conditions; nevertheless, it was also analyzed. Two examples of the closest relations are presented in Figure 3.

^{2}of their description by the 4th degree power functions was used. The square root of this index was conventionally taken as the correlation ratio η. In fact, such a high order of the function allows us to take its confidence index as a value very close to η

^{2}(the confidence indexes for the 4th, 5th and 6th degrees power functions are almost the same for all parameters). The closer to a linear relation, the closer R

^{2}and η

^{2}are to each other. For all parameters, it was determined that (η

^{2}–R

^{2}) is <0.1, except for only one comparison between ΔT

_{AV}and T daily amplitude, where the obtained value is 0.12. For all other parameters, the maximal value of (η

^{2}–R

^{2}) is only 0.06 for ΔT

_{AV}and 0.07 for ΔT

_{MAX}. Thus, except for just one relation, the non-linearity is negligibly small, so that R is a real indicator of the closeness of the relation.

_{MAX}and ΔT

_{AV}with low cloudiness only for 3 p.m. leads to insignificant (<0.1) values of R, which is also not surprising, since the contribution of counter radiation to the daytime radiation balance is small.

_{S}, the daily amplitude of the air temperature T, total cloudiness and the lowest relative humidity during the day. The relations of the UHI intensity with various wind speed parameters are weaker, especially for wind speeds at high altitudes, while the relationships of the UHI intensity with the highest air pressure and precipitation are weak. A similar result on the weakening of the relationship between the UHI intensity and V with increasing altitude when comparing data at a height of 90 and 252 m was obtained in Vienna, Austria, in Böhm and Gabl [17]. Thus, the wind speed affects the UHI intensity in Moscow less than cloudiness, in contrast to the conditions of Ibadan, Nigeria according to Anibaba et al. [16].

_{R}is the R error; n is the sample size. As we know, the relation may be considered significant if t > 3. As can be seen from Table 2, according to Student’s criterion, even the weakest relations are significant with an extremely high probability (much higher than 0.999). It should be noted that the t criterion is parametric and can be used if the distribution follows the Normal law. As shown by the authors in [7], the distributions of the UHI intensity are close to the Normal law in summer, while in winter they have a significant positive asymmetry. However, for large (n > 100) samples and R values far from 0 and 1, its application is possible even without a special estimation of the type of distribution according to Isaev [25]. Thus, given the large sample size even for sodar data about wind, we use the t-criterion correctly. However, for greater confidence in the conclusions, we also consider another non-parametric Fisher’s z-criterion:

_{z}are only a bit lower than absolute values of t. All correlation coefficients according to the z-criterion (4) are also reliable with an extremely high probability at a significance level α→0 close to zero. Thus, any of the considered meteorological parameters significantly affect the UHI intensity.

_{X·YZ}was also calculated between the maximal UHI intensity (X) and the two parameters which affect it more strongly than all other ones: low cloudiness (Y) and the mean daily wind speed at 15 m (Z). As we know, this coefficient indicates the simultaneous dependence of X on both parameters Y and Z, i.e., the closeness of a linear correlation between the maximal UHI intensity and the combined effect of both low cloudiness and mean daily wind speed. On average, over three years, it was determined to be 0.76. It should be noted that a separate calculation of the same R

_{X·YZ}only for the data of 2018 leads to its even higher value: 0.82. It is notable that similar multiple correlation coefficient between the UHI intensity, wind speed and cloudiness in Vienna was much lower: only from 0.40 to 0.47 according to Böhm and Gabl [17]. It may be that they used the data on total cloudiness and, therefore, the relation was weaker.

_{X·YZ}values is evident for α→0.

#### 3.3. Empirical Functions of Meteorological Parameters

_{MAX}values (the difference between the Balchug station and the average of 13 out of 14 rural stations in Moscow region, except for Nemchinovka).

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**Statistical relation between the maximal and average UHI intensities in Moscow for every 3 h during 2018–2020.

**Figure 2.**Air temperature maps over Moscow region. (

**a**) Mean isotherms on average for the period 2018–2020; (

**b**) Isotherms on 23 February 2018, at 06 a.m. Margins of Moscow city (until 2012) and Moscow region are shown by double black lines. Manned and automatic weather stations are shown by black and white circles, respectively. B—Balchug station; M—Mikhelson observatory; U—MSU MO; N—Nemchinovka station.

**Figure 3.**Graphs of statistical relations between the maximal UHI intensity, low cloudiness (

**left**) and daily air temperature amplitude (

**right**). Moscow, 2018–2020.

**Figure 4.**Empirical functions of the UHI intensity of daily averaged meteorological parameters in 2018–2020, Moscow. (

**a**) Function of the air temperature; (

**b**) functions of the wind velocity; (

**c**) functions of the cloudiness. Confidence intervals are calculated with the 0.95 confidence probability.

**Table 1.**Statistical relations between meteorological parameters and the UHI intensity. Moscow, 2018–2020. The first values are the correlation coefficients; second values in brackets are close to the correlation ratios. Bold values indicate the closest relations.

Parameter Which Can Influence the UHI | Sample Size | Average UHI Intensity | Maximal UHI Intensity |
---|---|---|---|

Total cloudiness, daily sum; 2018–2020 | 1096 | −0.43 (0.45) | −0.54 (0.54) |

Low cloudiness, daily sum; 2018–2020 | 1096 | −0.56 (0.59) | −0.67 (0.70) |

Mean daily wind speed at 15 m by the vane M-63 data, m/s; 2018–2020 | 1096 | −0.39 (0.40) | −0.49 (0.50) |

Mean daily wind speed in the 40–200 m air layer by the SODAR data, m/s; 2018–2019 | 729 | −0.33 (0.34) | −0.41 (0.41) |

Maximal daily wind speed among 8 values averaged over 10 min at 15 m by the vane M-63 data, m/s; 2018–2020 | 1096 | −0.28 (0.29) | −0.33 (0.34) |

Maximal wind gust during a day at 15 m by the vane M-63 data, m/s; 2018–2020 | 1096 | −0.36 (0.37) | −0.36 (0.36) |

Air temperature daily amplitude, °C; 2018–2020 | 1096 | 0.39 (0.47) | 0.60 (0.64) |

Surface temperature daily amplitude, °C; 2018–2020 | 1096 | 0.43 (0.49) | 0.64 (0.68) |

The highest air pressure during the day, hPa; 2018–2020 | 1096 | 0.37 (0.37) | 0.29 (0.29) |

The lowest relative humidity during the day, %; 2018–2020 | 1096 | −0.42 (0.48) | −0.54 (0.59) |

Precipitation amount, mm; 2018–2020 | 1096 | −0.31 (0.38) | −0.28 (0.35) |

**Table 2.**Checking the statistical significance of the relationship between meteorological parameters and UHI intensity. Moscow, 2018–2020. The first values are t-test values; the second ones in brackets are the z-test values. Bold values indicate the closest relations.

Parameter Which Can Influence the UHI | Sample Size | Average UHI Intensity | Maximal UHI Intensity |
---|---|---|---|

Total cloudiness, daily sum; 2018–2020 | 1096 | −15.9 (−15.4) | −21.0 (−19.8) |

Low cloudiness, daily sum; 2018–2020 | 1096 | −22.1 (−20.7) | −30.0 (−26.9) |

Mean daily wind speed at 15 m by the vane M-63 data, m/s; 2018–2020 | 1096 | −13.8 (−13.5) | −18.5 (−17.7) |

Mean daily wind speed in the 40–200 m air layer by the SODAR data, m/s; 2018–2019 | 729 | −9.4 (−9.2) | −12.1 (−11.7) |

Maximal daily wind speed among 8 values averaged over 10 min at 15 m by the vane M-63 data, m/s; 2018–2020 | 1096 | −9.7 (−9.6) | −11.4 (−11.2) |

Maximal wind gust during a day at 15 m by the vane M-63 data, m/s; 2018–2020 | 1096 | −12.7 (−12.4) | −12.7 (−12.4) |

Air temperature daily amplitude, °C; 2018–2020 | 1096 | 13.9 (13.5) | 24.6 (22.8) |

Surface temperature daily amplitude, °C; 2018–2020 | 1096 | 15.8 (15.3) | 27.7 (25.2) |

The highest air pressure during the day, hPa; 2018–2020 | 1096 | 13.1 (12.8) | 10.1 (10.0) |

The lowest relative humidity during the day, %; 2018–2020 | 1096 | −15.4 (−14.9) | −21.5 (−20.2) |

Precipitation amount, mm; 2018–2020 | 1096 | −10.7 (−10.5) | −9.8 (−9.6) |

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**MDPI and ACS Style**

Lokoshchenko, M.A.; Alekseeva, L.I.
Influence of Meteorological Parameters on the Urban Heat Island in Moscow. *Atmosphere* **2023**, *14*, 507.
https://doi.org/10.3390/atmos14030507

**AMA Style**

Lokoshchenko MA, Alekseeva LI.
Influence of Meteorological Parameters on the Urban Heat Island in Moscow. *Atmosphere*. 2023; 14(3):507.
https://doi.org/10.3390/atmos14030507

**Chicago/Turabian Style**

Lokoshchenko, Mikhail A., and Lyubov I. Alekseeva.
2023. "Influence of Meteorological Parameters on the Urban Heat Island in Moscow" *Atmosphere* 14, no. 3: 507.
https://doi.org/10.3390/atmos14030507