# Estimation of fine-scale relative humidity profiles: an issue for understanding the atmospheric water cycle

Veronique Michot, Hélène Brogniez, Mathieu Vrac, Soulivanh Thao, Helene Chepfer, Pascal Yiou, Christophe Dufour

<figure><img src="image_1.png" /></figure>

## To cite this version:

Veronique Michot, Hélène Brogniez, Mathieu Vrac, Soulivanh Thao, Helene Chepfer, et al.. Estimation of fine-scale relative humidity profiles: an issue for understanding the atmospheric water cycle. EGU General Assembly 2020, May 2020, Online, Unknown Region. pp.21947, 10.5194/egusphere-egu2020-21947. insu-04447520

HAL Id: insu-04447520

https://insu.hal.science/insu-04447520

Submitted on 4 Jun 2024

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<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Estimation of fine-scale relative humidity profiles: an issue for understanding the atmospheric water cycle

Veronique Michot, Hélène Brogniez, Mathieu Vrac, Soulivanh Thao, Helene Chepfer, Pascal Yiou, Christophe Dufour

► To cite this version:

Veronique Michot, Hélène Brogniez, Mathieu Vrac, Soulivanh Thao, Helene Chepfer, et al.. Estimation of fine-scale relative humidity profiles: an issue for understanding the atmospheric water cycle. EGU General Assembly 2020, May 2020, Online, Unknown Region. pp.21947, 10.5194/egusphere-egu2020-21947. insu-04447520

HAL Id: insu-04447520

https://insu.hal.science/insu-04447520v1

Submitted on 4 Jun 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Estimation of fine-scale relative humidity profiles: an issue for understanding the atmospheric water cycle

V. MICHOT, G. CARELLA, H. BROGNIEZ, M. VRAC, H. CHEPFER, P. YIOU, S. THAO, C. DUFOUR

<figure><img src="image_3.png" /></figure>

## EGU, Vienne, May 7th 2020

<figure><img src="image_4.png" /></figure>

LATMOS

<figure><img src="image_5.png" /></figure>

LSCE

<figure><img src="image_6.png" /></figure>

LMD

SPACE OBS

<figure><img src="image_7.png" /></figure>

Institut Pierre Simon Laplace

<figure><img src="image_8.png" /></figure>
Estimation of fine-scale relative humidity profiles:
an issue for understanding the atmospheric water cycle

V. MICHOT, G. CARELLA, H. BROGNIEZ, M. VRAC,
H. CHEPFER, P. YIOU, S. THAO, C. DUFOUR

<figure><img src="image_2.png" /></figure>

EGU, Vienne, May 7th 2020

<figure><img src="image_3.png" /></figure>

LATMOS

<figure><img src="image_4.png" /></figure>

LSCE

<figure><img src="image_5.png" /></figure>

LMD

SPACE OBS

<figure><img src="image_6.png" /></figure>

Institut
Pierre
Simon
Laplace

<figure><img src="image_7.png" /></figure>
## Scientific issue

Microphysical processes driving cloud -and water vapour- related distribution and variability in the troposphere are not well known

<figure><img src="image_9.png" /></figure>

an important source of uncertainty in climate models

## Data

The micro-physical properties described by the satellite CALIPSO (Scattering Ratio/SR = large cloud droplet size, non precipitating clouds) [1, 2] and CloudSat (Radar Reflectivity/RR = small particles) [3], can be strongly influenced by the Relative Humidity (RH, in %), described by the SAPHIR [4] satellite in the whole troposphere, but :

<figure><img src="image_10.png" /></figure>

these instruments have different spatial resolutions :

- Megha-Tropiques (2011) : SAPHIR micro-wave radiometer, 10km

- A-Train constellation (2006) : CALIPSO, 90m ; CloudSat 1,4km

<figure><img src="image_11.png" /></figure>

clouds are heterogeneous variables ≠ humidity is a continuous field

<figure><img src="image_12.png" /></figure>

## References:

[1] Brogniez H. et al., 2015, doi:10.1175/JAMC-D-14-0096.1

[2] Brogniez, H. et al., 2016, doi:10.1175/JTECH-D-15-0237.1, 2016

[3] Marchand, R. et al., 2008, https://doi.org/10.1175/2007JTECHA1006.1

[4] Chepfer, H. et al., 2010, doi: 10.1029/2009JD012251

<figure><img src="image_13.png" /></figure>
Scientific issue

Microphysical processes driving cloud -and water vapour- related distribution and variability in the troposphere are
not well known

<figure><img src="image_8.png" /></figure>

an important source of uncertainty in climate models

Data

The micro-physical properties described by the satellite CALIPSO (Scattering Ratio/SR = large cloud droplet size, non precipitating clouds) [1, 2] and CloudSat (Radar Reflectivity/RR = small particles) [3], can be strongly influenced by the Relative Humidity (RH, in %), described by the SAPHIR [4] satellite in the whole troposphere, but :

<figure><img src="image_9.png" /></figure>

these instruments have different spatial resolutions :

- Megha-Tropiques (2011) : SAPHIR micro-wave radiometer, 10km

- A-Train constellation (2006) : CALIPSO, 90m ; CloudSat 1,4km

<figure><img src="image_10.png" /></figure>

clouds are heterogeneous variables ≠ humidity is a continuous field

<figure><img src="image_11.png" /></figure>

References:

[1] BrogniezH. et al., 2015, doi:10.1175/JAMC-D-14-0096.1

[2] Brogniez, H.et al., 2016, doi:10.1175/JTECH-D-15-0237.1, 2016

[3] Marchand, R. et al., 2008, https://doi.org/10.1175/2007JTECHA1006.1

[4] Chepfer, H. et al., 2010, doi: 10.1029/2009JD012251

<figure><img src="image_12.png" /></figure>
# Relative humidity and cloud structure data

<figure><img src="image_14.png" /><figcaption>SAPHIR :
Relative Humidity for 6 pressure levels</figcaption></figure>

X axis : SAPHIR-CALIPSO-CloudSat co-located pixels

Y axis : altitude or pressure level

<figure><img src="image_15.png" /><figcaption>CALIPSO :
Optically thin and non precipitating clouds</figcaption></figure>

Signal fully attenuated by the
rain...

<figure><img src="image_16.png" /><figcaption>CloudSat :
Optically thick and potentially rainy clouds</figcaption></figure>

... detected by the
radar

<figure><img src="image_17.png" /></figure>
# Relative humidity and cloud structure data

<figure><img src="image_13.png" /><figcaption>SAPHIR :
Relative Humidity for 6 pressure levels</figcaption></figure>

X axis : SAPHIR-CALIPSO-CloudSat co-located pixels

Y axis : altitude or pressure level

<figure><img src="image_14.png" /><figcaption>CALIPSO :
Optically thin and non precipitating clouds</figcaption></figure>

Signal fully attenuated by the
rain...

<figure><img src="image_15.png" /><figcaption>CloudSat :
Optically thick and potentially rainy clouds</figcaption></figure>

... detected by the
radar

<figure><img src="image_16.png" /></figure>
Generate possible RH profiles at the scale of the cloud profiles (90m) that would be linked to the different types of clouds with different microphysical properties

<figure><img src="image_18.png" /></figure>

## statistical downscaling

$$RH_l \sim (\{SR_1, SR_2, ..., SR_s\}, \{PH_1, PH_2, ..., PH_s\}, \{RR_1, RR_2, ..., RR_r\})$$

Where:

- RHl is the 10km-RH resolution of SAPHIR at atmospheric layer (l=1 to 6)

- SR₁...s is the Scattering Ratio of CALIPSO (s=40)

- PH₁...s is the Phase cloud from SR of CALIPSO (s=40)

- RR₁...r is the Radar Reflectivity from CloudSat (r=5)

<figure><img src="image_19.png" /></figure>
Generate possible RH profiles at the scale of the cloud profiles (90m) that would be linked to the different types of clouds with different microphysical properties

## statistical downscaling

$$RH_l \sim (\{SR_1, SR_2, ..., SR_s\}, \{PH_1, PH_2, ..., PH_s\}, \{RR_1, RR_2, ..., RR_r\})$$

Where:

- RHl is the 10km-RH resolution of SAPHIR at atmospheric layer (l=1 to 6)

- SR₁...s is the Scattering Ratio of CALIPSO (s=40)

- PH₁...s is the Phase cloud from SR of CALIPSO (s=40)

- RR₁...r is the Radar Reflectivity from CloudSat (r=5)

<figure><img src="image_17.png" /></figure>
# Design of the downscaling model

Following Carella et al. 2020 [6]:

CALIPSO data & 1 month (July 2013) & 1 ocean (Indian Ocean) & 1 cloud type (ice phase clouds)

<figure><img src="image_20.png" /></figure>

Quantile Regression Forest algorithm (iteratively applied) [7]

<figure><img src="image_21.png" /><figcaption>Mean SR profiles per cluster derived by combining
the cloud phase flags in Cesana and Chepfer [7]</figcaption></figure>

## References:

[6] Carella G. et al., 2020, ESSD, https://doi.org/10.5194/essd-2018-138

[7] Meinshausen, N.: Quantile regression forests, J. Mach. Learn. Res., 7, 983 999, 2006

[8] Cesana et Chepfer, 2013, doi: 30 10.1002/jgrd.50376

<figure><img src="image_22.png" /></figure>
# Design of the downscaling model

Following Carella et al. 2020 [6]:

CALIPSO data & 1 month (July 2013) & 1 ocean (Indian Ocean) & 1 cloud type (ice phase clouds)

<figure><img src="image_18.png" /></figure>

Quantile Regression Forest algorithm (iteratively applied) [7]

<figure><img src="image_19.png" /><figcaption>Mean SR profiles per cluster derived by combining
the cloud phase flags in Cesana and Chepfer [7]</figcaption></figure>

References:

[6] Carella G. et al., 2020, ESSD, https://doi.org/10.5194/essd-2018-138

[7] Meinshausen, N.: Quantile regression forests, J. Mach. Learn. Res., 7, 983–999, 2006

[8] Cesana et Chepfer, 2013, doi: 30 10.1002/jgrd.50376

<figure><img src="image_20.png" /></figure>
# Design of the downscaling model

Here we combine the information provided by CALIPSO and CloudSat to generalize the method to all cloud types for July 2013 over the Indian Ocean

**Mass Balance:** Absolute differences between the HR observed in each SAPHIR pixel and the average of the HR values it contains.

<figure><img src="image_23.png" /><figcaption>**SAPHIR coarse resolution**</figcaption></figure>

<figure><img src="image_24.png" /><figcaption>**Mass balance**</figcaption></figure>

<figure><img src="image_25.png" /></figure>

<figure><img src="image_26.png" /><figcaption>**Dowscaled SAPHIR**</figcaption></figure>

<figure><img src="image_27.png" /><figcaption>**Predicted Relative Humidity uncertainty(%)**
**fine resolution**</figcaption></figure>

<figure><img src="image_28.png" /></figure>

<figure><img src="image_29.png" /></figure>
# Design of the downscaling model

Here we combine the information provided by CALIPSO and CloudSat to generalize the method to all cloud types for July 2013 over the Indian Ocean

**Mass Balance:** Absolute differences between the HR observed in each SAPHIR pixel and the average of the HR values it contains.

<figure><img src="image_21.png" /><figcaption>**SAPHIR coarse resolution**</figcaption></figure>

<figure><img src="image_22.png" /><figcaption>**Mass balance**</figcaption></figure>

<figure><img src="image_23.png" /></figure>

<figure><img src="image_24.png" /><figcaption>**Dowscaled SAPHIR**</figcaption></figure>

<figure><img src="image_25.png" /><figcaption>**Predicted Relative Humidity uncertainty(%)**
**fine resolution**</figcaption></figure>

<figure><img src="image_26.png" /></figure>

<figure><img src="image_27.png" /></figure>
Model reproducibility:
spatial and temporal robustness

The distribution of Relative Humidity shows for each SAPHIR layer a variability between oceans and seasons.

We are therefore seeking to know the impact that this spatial and temporal variability can have on the method in order to know to what extent it is possible to generalize it.

PDF of the RH for all the month of July (left column) and December (right column) between 2012 and 2016 over Indian Ocean (solid blue line), South Atlantic (solid red line), North Atlantic (solid dark line) and for July and December 2013 over Indian Ocean (dashed blue line), South Atlantic (dashed red line), North Atlantic (dashed black line)

<figure><img src="image_30.png" /></figure>
Model reproducibility:
spatial and temporal robustness

The distribution of Relative Humidity shows for each SAPHIR layer a variability between oceans and seasons.

We are therefore seeking to know the impact that this spatial and temporal variability can have on the method in order to know to what extent it is possible to generalize it.

PDF of the RH for all the month of July (left column) and December (right column) between 2012 and 2016 over Indian Ocean (solid blue line), South Atlantic (solid red line), North Atlantic (solid dark line) and for July and December 2013 over Indian Ocean (dashed blue line), South Atlantic (dashed red line), North Atlantic (dashed black line)

<figure><img src="image_28.png" /></figure>
# Sensitivity of the method to the training dataset : regional and temporal tests

The model based on learning relative humidity profiles over one ocean gives accurate predictions over the same region, but does not correctly predict RH profiles over another ocean. These results could be explained, in part, by spatial differences in the distribution of RH

<table><caption>Coefficient of determination (R2) scores of downscaling for each RHᵢ with the QRF method for several kind of calibration strategies</caption><thead><tr><th colspan="2">Sample calibration</th><th colspan="2">Sample prediction</th><th colspan="6">R<sup>2</sup></th></tr><tr><th>Ocean</th><th>Date</th><th>Ocean</th><th>Date</th><th>100:200 hPa</th><th>250:350 hPa</th><th>400:600 hPa</th><th>650:700 hPa</th><th>750:800 hPa</th><th>850:950 hPa</th></tr></thead><tbody><tr><td rowspan="3">Indian Ocean</td><td rowspan="3">July 2013</td><td rowspan="2">Indian Ocean</td><td>July 2013</td><td>0.76</td><td>0.80</td><td>0.76</td><td>0.74</td><td>0.75</td><td>0.66</td></tr><tr><td>December 2013</td><td>0.18</td><td>0.34</td><td>0.29</td><td>0.16</td><td>0.18</td><td>0.01</td></tr><tr><td>Tropical North Atlantic</td><td>July 2013</td><td>0.07</td><td>0.18</td><td>0.20</td><td>0.25</td><td>0.21</td><td>0.02</td></tr><tr><td rowspan="2">Tropical North Atlantic</td><td rowspan="2">July 2013</td><td rowspan="2">Tropical North Atlantic</td><td>July 2013</td><td>0.65</td><td>0.73</td><td>0.72</td><td>0.74</td><td>0.73</td><td>0.69</td></tr><tr><td>December 2013</td><td>0.12</td><td>0.26</td><td>0.24</td><td>0.15</td><td>0.13</td><td>0.02</td></tr></tbody></table>

<figure><img src="image_31.png" /><figcaption>Scatter plot of the predicted RH (y-axis) vs the original SAPHIR RH (x-axis) for the six atmospheric layers, for the test sample of July 2013.</figcaption></figure>

<figure><img src="image_32.png" /></figure>
# Sensitivity of the method to the training dataset : regional and temporal tests

The model based on learning relative humidity profiles over one ocean gives accurate predictions over the same region, but does not correctly predict RH profiles over another ocean. These results could be explained, in part, by spatial differences in the distribution of RH

<table><caption>Coefficient of determination (R2) scores of downscaling for each RH<sub>i</sub> with the QRF method for several kind of calibration strategies</caption><thead><tr><th colspan="2">Sample calibration</th><th colspan="2">Sample prediction</th><th colspan="6">R<sup>2</sup></th></tr><tr><th>Ocean</th><th>Date</th><th>Ocean</th><th>Date</th><th>100:200 hPa</th><th>250:350 hPa</th><th>400:600 hPa</th><th>650:700 hPa</th><th>750:800 hPa</th><th>850:950 hPa</th></tr></thead><tbody><tr><td rowspan="3">Indian Ocean</td><td rowspan="3">July 2013</td><td rowspan="2">Indian Ocean</td><td>July 2013</td><td>0.76</td><td>0.80</td><td>0.76</td><td>0.74</td><td>0.75</td><td>0.66</td></tr><tr><td>December 2013</td><td>0.18</td><td>0.34</td><td>0.29</td><td>0.16</td><td>0.18</td><td>0.01</td></tr><tr><td>Tropical North Atlantic</td><td>July 2013</td><td>0.07</td><td>0.18</td><td>0.20</td><td>0.25</td><td>0.21</td><td>0.02</td></tr><tr><td rowspan="2">Tropical North Atlantic</td><td rowspan="2">July 2013</td><td rowspan="2">Tropical North Atlantic</td><td>July 2013</td><td>0.65</td><td>0.73</td><td>0.72</td><td>0.74</td><td>0.73</td><td>0.69</td></tr><tr><td>December 2013</td><td>0.12</td><td>0.26</td><td>0.24</td><td>0.15</td><td>0.13</td><td>0.02</td></tr></tbody></table>

<figure><img src="image_29.png" /><figcaption>Scatter plot of the predicted RH (y-axis) vs the original SAPHIR RH (x-axis) for the six atmospheric layers, for the test sample of July 2013.</figcaption></figure>

<figure><img src="image_30.png" /></figure>
# Evaluation of the downscaled RH values with in-situ measurements

Evaluation of the high-resolution RH performed by the comparison of the cumulative distribution functions (CDF) of the original low resolution RH, the predicted high resolution RH, the RH from NARVAL and the RH from the Météo-France radiosonde, for July and August 2013 over the tropical North Atlantic.

CDFs of RH at the original 10km resolution and the prediction at the 90m resolution are very close and relatively close to radiosonde and Narval at lower atmosphere. But, RH CDFs highly differ from the Narval RH that show a drier troposphere at middle altitudes.

NARVAL : data from an airborne water vapor lidar that observed the tropical North Atlantic atmosphere.

Météo-France radiosonde: data from the EUREC4A (Elucidating the role of clouds-circulation coupling in climate) international campaign that observed the tropical North Atlantic atmosphere near the Barbados.

<figure><img src="image_33.png" /><figcaption>CDFs of SAPHIR RH (blue line), predicted RH (red line), NARVAL data (green line) and Météo-France radiosonde data (black line), for August 2013 over the tropical North Atlantic.</figcaption></figure>

<figure><img src="image_34.png" /></figure>
# Evaluation of the downscaled RH values with in-situ measurements

Evaluation of the high-resolution RH performed by the comparison of the cumulative distribution functions (CDF) of the original low resolution RH, the predicted high resolution RH, the RH from NARVAL and the RH from the Météo-France radiosonde, for July and August 2013 over the tropical North Atlantic.

CDFs of RH at the original 10km resolution and the prediction at the 90m resolution are very close and relatively close to radiosonde and Narval at lower atmosphere. But, RH CDFs highly differ from the Narval RH that show a drier troposphere at middle altitudes.

NARVAL : data from an airborne water vapor lidar that observed the tropical North Atlantic atmosphere.

Météo-France radiosonde: data from the EUREC4A (Elucidating the role of clouds-circulation coupling in climate) international campaign that observed the tropical North Atlantic atmosphere near the Barbados.

<figure><img src="image_31.png" /><figcaption>CDFs of SAPHIR RH (blue line), predicted RH (red line), NARVAL data (green line) and Météo-France radiosonde data (black line), for August 2013 over the tropical North Atlantic.</figcaption></figure>

<figure><img src="image_32.png" /></figure>
-> Establish the level of possible spatial generalization of the method

-> adaptation and application of the method to the continents

-> Use of the new HR dataset for the study of multi-scale interactions: stratocumulus to cumulus transition profile; low cloud and HR feedback in trade wind areas; processes associated with MJO...

<figure><img src="image_35.png" /></figure>
Work in Progress

-> Establish the level of possible spatial generalization of the method

Perspectives

-> adaptation and application of the method to the continents

-> Use of the new HR dataset for the study of multi-scale interactions: stratocumulus to cumulus transition profile; low cloud and HR feedback in trade wind areas; processes associated with MJO...

<figure><img src="image_33.png" /></figure>
<figure><img src="image_36.png" /></figure>

<figure><img src="image_37.png" /></figure>
<figure><img src="image_34.png" /></figure>

<figure><img src="image_35.png" /></figure>