Mikhail Varentsov Lomomosov Moscow State University, Faculty of - - PowerPoint PPT Presentation

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Mikhail Varentsov Lomomosov Moscow State University, Faculty of - - PowerPoint PPT Presentation

Dec 13, 2022 304 likes •649 views

Experimental urban heat island research for Arctic cities: Methods of measurements and data processing Mikhail Varentsov Lomomosov Moscow State University, Faculty of Geography, Department of Meteorology and Climatology; Plan of presentation:

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Experimental urban heat island research for Arctic cities:

Methods of measurements and data processing

Mikhail Varentsov

Lomomosov Moscow State University, Faculty of Geography, Department of Meteorology and Climatology;

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Plan of presentation:

1. Measuring techniques & data processing 2. Geostatistical methods 3. Application for urban heating system 4. Remote sensing 5. Newest results for Apatity 2015

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Measuring methods

AWS

Mobile AWD

iButton sensors MTP-5 temperature profiler Joining the data, synchronizing, geostatistical modelling

Visualization and analisys

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Sensors location: Norisk

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Apatity Murmansk

АМС фон АМС центр ГМС ГМС АМС центр АМС фон

Sensors location: Murmans & Apatity

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Data processing

The problem: joining the data from AWS (relatively reliable) and iButtons (less reliable) Solution:

  • Verification of the iButtons before the

experiment – static correction for each sensor

  • In points with AWS we also install iButtons –

dynamic correction for control sensors

  • Spatial interpolation of the dynamic correction

for other sensors

𝑼 = 𝑼𝒕𝒔𝒅 + ∆𝑼 + ∆𝑼𝒆𝒛𝒐(𝒖, 𝒚)

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Verification by mobile AWS

Apatity 29 января, ночь Apatity 31 января, день Apatity 2 февраля, ночь Murmaks 30 января, вечер

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Verification by mobile AWS

Norilsk

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Measuring results: Murmansk and Apatity

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Темрература, ⁰С Время суток, часы

Апатиты

Самая холодная точка (к западу от города) Фоновая АМС АМС в центре города Метеостанция "Апатиты"

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Температура воздуха, ⁰С Время суток, часы

Мурманск

Фоновая АМС АМС в центре Самая холодная точка (к югу от города) Метеостанция "Мурманск"

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Температура воздуха, ⁰С Время суток, часы

Норильск

Центр города Самая теплая точка (берег оз. Долгое) Метеостанция "Норильск" АМС на окраине города

Measuring results: Norilsk

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Plan of presentation:

1. Measuring techniques & data processing 2. Geostatistical methods 3. Application for urban heating system 4. Remote sensing 5. Newest results for Apatity 2015

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The question: how to provide spatial distribution

  • f the temperature in the city and surrounding?
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What is kriging?

𝒂∗ 𝒚 =

𝒋=𝟐 𝒐

𝝁𝒋𝒂 𝒚𝒋 Where:

𝑎∗ 𝑦 - prediction at unknown point 𝑦 𝑎 𝑦𝑗 - known value at point 𝑦𝑗 𝝁𝒋: depends on spatial self-correlation (variogram)

Features & Advantages:

  • Exact estimation
  • Best linear unbiased estimator
  • Also provide the field of estimation variance 𝜏2
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What is variogram?

Function describing the degree of spatial dependence of a spatial random field

  • f stochastic process:

𝜹 𝒊 = 𝟐 𝟑𝑶(𝒊)

𝒋=𝟐 𝑶(𝒊)

[𝒂 𝒚𝒋 − 𝒂(𝒚𝒋 + 𝒊)]𝟑

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Kriging algorithm

Building experimental variogram Fitting theoretical variogram model to experimental variogram Building estimation field Power-law Gaussian Exponential

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Apatity

Maps of the temperature anomaly

Mean Max UHI

9-10 ⁰С

острова тепла

2-3 ⁰С

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Mean Max UHI Max UHI

Murmansk

Maps of the temperature anomaly

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Method for heterogeneous terrain

𝑈 𝑦, 𝑧, 𝑢 = 𝑈0 ℎ 𝑦, 𝑧 , 𝑢 + ∆𝑈 (𝑦, 𝑧, 𝑢)

Atmosphere temperature at fixed height Correction for certain moment MTP-temperature Kriging

𝒊 𝒚, 𝒛 from ASTER DEM

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Norilsk

Mean Max

6-7 ⁰С ≈ 2 ⁰С

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Plan of presentation:

1. Measuring techniques & data processing 2. Geostatistical methods 3. Application for urban heating system 4. Remote sensing 5. Newest results for Apatity 2015

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Air temperature

Statistical model of heating station

Direct and reverse water temperature difference Heat production

Effect on urban economy

y = -0.9153x + 24.015

10 20 30 40 50

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5

Разница температуры прямой и обратной воды, ⁰С Температура наружного воздуха, ⁰С

y = 154.65x + 1888.4

4000 5000 6000 7000 8000 9000 10000 20 30 40 50

Суточный отпуск тепла, ГКал

Разница температуры прямой и обратной воды, ⁰С

Apatity (power 590 Гкал/ч): 1 ⁰С → ≈ 33 т. tons of coal / day What is 1 ⁰С for the power station? Norilsk ТЭЦ-1 (power 2321 Гкал/ч):

1 ⁰С → ≈ 81 000 m3 of the nature gas / day примерно 85 тыс. рублей в день или

24 млн. руб. в год

(При стоимости угля 1300 руб. /т и продолжительность отопительного сезона 272 днея)

Potential effect of UHI 2 ⁰С:

примерно 600 тыс. рублей в день или

180 млн. руб. в год

(При стоимости газа 4.14 руб./м3 и продолжительность отопительного сезона 300 дней)

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Температура, ⁰C

Время суток, ч

Средняя температура в городе Температура по данным ТЭЦ

Средняя ошибка:

0.4 ⁰С

≈ 37 млн. руб. в год

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29 января 30 января 31 января 1 февраля

Среднесуточная температура, ⁰С ТЭЦ Город

Apatity Norilsk

Средняя ошибка:

1.1 ⁰С Temperature, measured by power station VS real temperature in the city

≈ 11 млн. руб. в год

Эти средства можно сэкономить, если оптимизировать систему температуры воздуха в городе

Effect on urban economy

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Plan of presentation:

1. Measuring techniques & data processing 2. Geostatistical methods 3. Application for urban heating system 4. Remote sensing 5. Newest results for Apatity 2015

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Norilsk

Mean Max

6-7 ⁰С ≈ 2 ⁰С

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MODIS for Norilsk

TERRA 17 January 2014

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MODIS for Norilsk

Mean data (TERRA) for November 2013 – January 2014

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R² = 0,3036

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2 4 6 8

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2 4 6 8

∆T по данным спутника TERRA ∆T по данным метеорологических наблюдений

TERRA

R² = 0,1317

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2 4 6 8

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2 4 6 8

∆T по данным спутника AQUA ∆T по данным метеорологических наблюдений

AQUA

Connection between surface and ground UHI

UHI Intensity: ∆𝑼 = 𝑼город − 𝑼фон

Satellite observations Observations (Power station – observatory)

VS

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Plan of presentation:

1. Measuring techniques & data processing 2. Geostatistical methods 3. Application for urban heating system 4. Remote sensing 5. Newest results for Apatity 2015

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Sensors location: Apatity 2015

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AWS results: Apatity 2015

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Max UHI

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New ideas for experimental research & data processing

1. Longer measuring campaign (one season at least) 2. On-line UHI monitoring (not iButtons, but GSM temperature sensors) 3. 3D UHI structure investigation (but at least 2 MTP-5 is needed) 4. Application of the Kriging with external drift for more detailed

  • analysis. What use as drift: LES data,

REG CM data, MODIS data, urban landuse data? 5. More comprehensive research about UHI and energy consumption

6. UHI temperature anomaly → anthropogenic heat flux

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Thank you for the attention