LATTIN: Lagrangian Atmospheric moisTure and heaT trackINg

The Lagrangian Atmospheric moisTure and heaT trackINg (LATTINv1.0.4) is a software developed in Python and Fortran for the study of moisture and heat sources. It has been developed within the SETESTRELO project at the EPhysLab (Environmental Physics Laboratory) at the University of Vigo.

LATTIN is a Python-based tool for Lagrangian atmospheric moisture and heat tracking

Moisture tracking methods

The following Table displays an overview of threshold parameters for identifying moisture sources for precipitation events. The * symbol indicates mean value of the variable. These threshold values have been obtained for Δt = 6 h.

Method	Precipitating parcels at target region	Moisture uptake			Moisture losses		Reference
Method	Precipitating parcels at target region	Δq (g/kg)	\|ΔRH\| (%)	ABL	Δq (g/kg)	RH (%)	Reference
SJ05				no			Stohl and James (2005)
SOD08	Δq < -0.2 & RH > 80%	Δq > 0.2		z<ABL	Δq < -0.2		Sodemann et al. (2008)
FAS19	Δq < -0.1 & RH > 80%	Δq > 0.1		no	Δq < -0.1		Freme and Sodemann (2019)
JK22	Δq < 0 & RH > 80%	Δq > 0	< 20	z<ABL_max	Δq < 0		Keune et al. (2022)
APA22	Δq < -0.1	Δq > 0		no	Δq < 0		Pérez-Alarcón et al (2022)

Heat tracking methods

Heat tracking is based on the dry static energy (DSE) or potential temperature (θ).The following table shows an overview of threshold parameters for heat tracking. In SCH19, the condition of the atmospheric boundary layer (ABL) must be satisfied at time t and t-6. These threshold values have been obtained for Δt = 6 h.

Method	Air parcels are warmed if	\|Δq\| (%)	\|ΔRH\| (%)	ABL	Reference
SCH19	ΔDSE >1 kJ	< 10		z<ABL_max	Schumacher et al. (2019)
SCH20	ΔDSE >1 kJ			z<ABL_max	Schumacher et al. (2020)
JK22	Δθ > 0 K		< 10	z<ABL_max	Keune et al. (2022)

The following figure shows the flowchart of the LATTIN algorithm.

For a more detailed understanding of LATTIN, Please refer to

Note

Pérez-Alarcón, A.; Fernández-Alvarez, J.C.; Nieto, R.; Gimeno, L. (2024). LATTIN: A Python-based tool for Lagrangian atmospheric moisture and heat tracking. Software Impacts, 20, 100638. https://doi.org/10.1016/j.simpa.2024.100638

References

Fremme, A. & Sodemann, H. (2019). The role of land and ocean evaporation on the variability of precipitation in the Yangtze River valley, Hydrol. Earth Syst. Sci., 23, 2525-2540, https://doi.org/10.5194/hess-23-2525-2019.

Keune, J., Schumacher, D.L., Miralles, D.G. (2022). A unified framework to estimate the origins of atmospheric moisture and heat using Lagrangian models. Geosci. Model Develop., 15(5), 1875-1898. Geosci. Model Dev., 15, 1875–1898. https://doi.org/10.5194/gmd-15-1875-2022

Pérez-Alarcón A, Sorí R, Fernández-Alvarez JC, Nieto R, Gimeno L (2022). Where does the moisture for North Atlantic tropical cyclones come from?. J. Hydrometeorol., 23:457–472. https://doi.org/10.1175/JHM-D-21-0117.1.

Schumacher, D.L., Keune, J., Van Heerwaarden, C.C., Vilà-Guerau de Arellano, J., Teuling, A.J., Miralles, D.G. (2019). Amplification of mega-heatwaves through heat torrents fuelled by upwind drought. Nat. Geosci., 12, 712–717. https://doi.org/10.1038/s41561-019-0431-6.

Schumacher, D. L., Keune, J., Miralles, D. G. (2020). Atmospheric heat and moisture transport to energy‐and water‐limited ecosystems. Ann. NY Acad. Sci., 1472, 123–138. https://doi.org/10.1111/nyas.14357

Sodemann H, Schwierz C, Wernli H. (2008). Interannual variability of Greenland winter precipitation sources: Lagrangian moisture diagnostic and North Atlantic Oscillation influence. J. Geophys. Res.-Atmos.; 113:D03107. https://doi.org/10.1029/2007JD008503.

Stohl A, James P A. (2005). A Lagrangian analysis of the atmospheric branch of the global water cycle: Part II: Earth’s river catchments ocean basins, and moisture transports between them. J. Hydrometeorol., 6:961–984. https://doi.org/10.1175/JHM470.1.