Quasi-Geostrophic diagnosis and QG-Omega inversion
The package of the QG-omega inversion codes calculates the QG forcing terms and the corresponding QG-omega components given GCM outputs or reanalysis. As an example, the package reproduces Fig. S1 of Nie et al 2020 (PNAS).
The package can be downloaded at https://zenodo.org/records/10957967
Ref: Nie, J.*, P.X. Dai, and A. H. Sobel, 2020: Dry and moist dynamics shape regional patterns of extreme precipitation sensitivity, Proc. Natl. Acad. Sci., 117, 8757-8763.
The package of the QG-omega inversion codes calculates the QG forcing terms and the corresponding QG-omega components given GCM outputs or reanalysis. As an example, the package reproduces Fig. S1 of Nie et al 2020 (PNAS).
The package can be downloaded at https://zenodo.org/records/10957967
Ref: Nie, J.*, P.X. Dai, and A. H. Sobel, 2020: Dry and moist dynamics shape regional patterns of extreme precipitation sensitivity, Proc. Natl. Acad. Sci., 117, 8757-8763.
Influence of convective organization in extreme precipitation event: (2022 Science China Earth Science)
A movie of unorganized and organized convections .
A movie of unorganized and organized convections .
| snapshots.mov |
Dependence of climate on atmospheric mass: (2020 JAS)
Supplementary information (pdf).
Citation: Xiong, J.Y., J. Yang, and J. Nie, 2020: Possible dependence of climate on atmospheric mass: a convection-circulation-cloud coupled feedback, accepted.
Supplementary information (pdf).
Citation: Xiong, J.Y., J. Yang, and J. Nie, 2020: Possible dependence of climate on atmospheric mass: a convection-circulation-cloud coupled feedback, accepted.
Characteristics of Extreme precipitation in ERA-Interim: (2020 JC)
Supplementary information (pdf).
Data for making the figures (amazon drive).
Citation: Dai, P, and J. Nie, 2020: A global quasi-geostrophic diagnosis of extratropical extreme precipitation, Journal of Climate, accepted.
Supplementary information (pdf).
Data for making the figures (amazon drive).
Citation: Dai, P, and J. Nie, 2020: A global quasi-geostrophic diagnosis of extratropical extreme precipitation, Journal of Climate, accepted.
Extreme precipitation QG analyses in CMIP5 simulations: (2020 PNAS)
The dataset of the extreme precipitation QG analyses in Nie et al. (2020) can be downloaded here (amazon drive).
We are pleased to share the related diagnostic data. However, these data are too large (~several Tb) to put on amazon drive (or other free platform). If you are interested in examining these data, please send me a Email, and we will figure out a way.
Citation: Nie, J., P. Dai, and A. H. Sobel, 2020: Dry and moist dynamics shape regional patterns of extreme precipitation sensitivity, Proc. Natl. Acad. Sci., 117, 8757-8763, https://doi.org/10.1073/pnas.1913584117.
The dataset of the extreme precipitation QG analyses in Nie et al. (2020) can be downloaded here (amazon drive).
We are pleased to share the related diagnostic data. However, these data are too large (~several Tb) to put on amazon drive (or other free platform). If you are interested in examining these data, please send me a Email, and we will figure out a way.
Citation: Nie, J., P. Dai, and A. H. Sobel, 2020: Dry and moist dynamics shape regional patterns of extreme precipitation sensitivity, Proc. Natl. Acad. Sci., 117, 8757-8763, https://doi.org/10.1073/pnas.1913584117.
Extreme precipitation events in East China and Southeastern US: (2019 JC)
The dataset of the extreme precipitation events in Nie and Fan (2019) can be downloaded here (amazon drive). Here is a readme file.
Citation: Nie, J. and B. Fan, 2019: Roles of dynamic forcings and diabatic heating in summer extreme precipitation in East China and the southeastern United States, Journal of Climate, 32, 5815-5831.
The dataset of the extreme precipitation events in Nie and Fan (2019) can be downloaded here (amazon drive). Here is a readme file.
Citation: Nie, J. and B. Fan, 2019: Roles of dynamic forcings and diabatic heating in summer extreme precipitation in East China and the southeastern United States, Journal of Climate, 32, 5815-5831.
Column Quasi-Geostrophic (CQG) modeling method: (2016 JAS)
CQG modeling method is a way to parameterize the interaction between convection and large-scale dynamics in the limited domain modeling (Nie and Sobel 2016). Convection may be represented by models with varying complexity, which are, from simple to complex, convective linear response functions (LRF, Kuang 2010), single-column models (SCM), and cloud-resolving models (CRM). Combining CQG with different representation of convection, there are CQG_LRF, CQG_SCM, and CQG_CRM.
I am happy to share these models for the research propose. If you will used them in your research, it would nice to send me a notice.
CQG_SCM
The SCM is the MIT single-column model (Emanuel and Zˇivkovic-Rothman 1999). It applies a buoyancy sorting convection scheme (Emanuel 1991) that represents an entire spectrum of convective clouds, as well as a stratiform cloud parameterization (Bony and Emanuel 2001).
CQG_SCM simulations are compared with CQG_LRF simulations in Nie and Sobel 2016, and compared with CQG_SCM simulations in Nie, Shaevitz, and Sobel, 2016 for a heavy rainfall case study.
(Download)
The download package includes the CQG_SCM model with the setting for the case study in Nie, Shaevitz, and Sobel, 2016.
CQG_CRM
codes (for SAM(system for atmospheric modeling))
CQG_LRF
upcoming...
Citation:
Nie, J. and A. H. Sobel, 2016: Modeling the Interaction between Quasi-Geostrophic Vertical Motion and Convection in a Single Column, Journal of the Atmospheric Sciences, 73, 1101-1117. doi:10.1175/JAS-D-15-0205.1.
Nie, J., A. H. Sobel, D. A. Shaevitz, and S. Wang, 2018: Dynamic Amplification of Extreme Precipitation Sensitivity, Proc. Natl. Acad. Sci., 115, 9467-9472.
CQG modeling method is a way to parameterize the interaction between convection and large-scale dynamics in the limited domain modeling (Nie and Sobel 2016). Convection may be represented by models with varying complexity, which are, from simple to complex, convective linear response functions (LRF, Kuang 2010), single-column models (SCM), and cloud-resolving models (CRM). Combining CQG with different representation of convection, there are CQG_LRF, CQG_SCM, and CQG_CRM.
I am happy to share these models for the research propose. If you will used them in your research, it would nice to send me a notice.
CQG_SCM
The SCM is the MIT single-column model (Emanuel and Zˇivkovic-Rothman 1999). It applies a buoyancy sorting convection scheme (Emanuel 1991) that represents an entire spectrum of convective clouds, as well as a stratiform cloud parameterization (Bony and Emanuel 2001).
CQG_SCM simulations are compared with CQG_LRF simulations in Nie and Sobel 2016, and compared with CQG_SCM simulations in Nie, Shaevitz, and Sobel, 2016 for a heavy rainfall case study.
(Download)
The download package includes the CQG_SCM model with the setting for the case study in Nie, Shaevitz, and Sobel, 2016.
CQG_CRM
codes (for SAM(system for atmospheric modeling))
CQG_LRF
upcoming...
Citation:
Nie, J. and A. H. Sobel, 2016: Modeling the Interaction between Quasi-Geostrophic Vertical Motion and Convection in a Single Column, Journal of the Atmospheric Sciences, 73, 1101-1117. doi:10.1175/JAS-D-15-0205.1.
Nie, J., A. H. Sobel, D. A. Shaevitz, and S. Wang, 2018: Dynamic Amplification of Extreme Precipitation Sensitivity, Proc. Natl. Acad. Sci., 115, 9467-9472.